Update app.py

app.py

@@ -1,1060 +1,422 @@
-"""
-╔══════════════════════════════════════════════════════════════╗
-║   PharmaBridge — Cross-Medical-System Drug Intelligence      ║
-║   Hugging Face Spaces  |  Gradio 4.x  |  Master's Thesis    ║
-╚══════════════════════════════════════════════════════════════╝
-7 Tabs:
-  1. Smart Drug Search         — TF-IDF cosine retrieval with cards UI
-  2. Cross-System Compare      — Side-by-side 5-system radar comparison
-  3. Dataset Analytics         — 3 sub-tabs of Plotly dashboards
-  4. Drug Fingerprint          — Single drug deep-dive profile
-  5. FDA Live Intelligence     — OpenFDA API (Labels / Events / NDC)
-  6. AI Medical Q&A            — HuggingFace Inference API (Mistral-7B)
-  7. Drug Explorer             — Paginated browse & filter table
-"""
-
 import gradio as gr
 import pandas as pd
 import numpy as np
-import plotly.graph_objects as go
 import plotly.express as px
-from plotly.subplots import make_subplots
-import joblib, re, os, requests, json, warnings
-warnings.filterwarnings("ignore")
-
+import plotly.graph_objects as go
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+import joblib
+
+# ==========================================================
+# PHARMABRIDGE — PREMIUM UI EDITION
+# Optimized for HuggingFace Spaces
+# ==========================================================
+
+# -----------------------------
+# LOAD DATA
+# -----------------------------
 
-# ────────────────────────────────────────────────────────────────
-# 0. LOAD / REBUILD MODELS
-# ────────────────────────────────────────────────────────────────
-
-def _clean(text):
-    if pd.isna(text): return ""
-    t = str(text).strip()
-    if t in ["FALSE","False","false","nan","NaN",""]: return ""
-    return re.sub(r"\s+"," ", re.sub(r"[^a-z0-9\s\+\-\./]"," ", t.lower())).strip()
-
-def _build_text(row):
-    s = row["medical_system"]
-    d = _clean(row.get("Dosages Description",""))
-    g = _clean(row.get("Generic Name and Strength",""))
-    b = _clean(row.get("Brand Name",""))
-    n = _clean(row.get("Generic Name",""))
-    if s == "Allopathic":   return " ".join(filter(None,[n,d,s.lower()]))
-    if s in ("Ayurvedic","Herbal"): return " ".join(filter(None,[g,d,s.lower()]))
-    if s == "Homeopathic":  return " ".join(filter(None,[b,d,s.lower()]))
-    return " ".join(filter(None,[g,d,s.lower()]))   # Unani
-
-print("⏳  Loading PharmaBridge models…")
 try:
-    VEC  = joblib.load("models/tfidf_vectorizer.pkl")
-    MAT  = joblib.load("models/tfidf_matrix.pkl")
-    DF   = pd.read_csv("models/drug_database.csv")
-    print("✅  PKL models loaded.")
-except Exception as e:
-    print(f"⚠️  PKL not found ({e}), rebuilding from CSV…")
-    raw = pd.read_csv("merged_pharma_dataset.csv")
-    DF  = raw.copy()
-    DF["drug_text"] = DF.apply(_build_text, axis=1)
-    DF = DF.rename(columns={
-        "Brand Name":"brand_name","Generic Name":"generic_name",
-        "Dosages Description":"dosage_form","Strength":"strength",
-        "Name of the Manufacturer":"manufacturer",
-        "Generic Name and Strength":"gns",
-    })
-    VEC = TfidfVectorizer(ngram_range=(1,2),max_features=15000,
-                          stop_words=None,sublinear_tf=True,min_df=1)
-    MAT = VEC.fit_transform(DF["drug_text"])
-    print("✅  Rebuilt from CSV.")
-
-# Normalise column names
-_REMAP = {"Brand Name":"brand_name","Generic Name":"generic_name",
-          "Dosages Description":"dosage_form","Strength":"strength",
-          "Name of the Manufacturer":"manufacturer","Generic Name and Strength":"gns"}
-for o,n in _REMAP.items():
-    if o in DF.columns and n not in DF.columns:
-        DF.rename(columns={o:n},inplace=True)
-for c in ["brand_name","generic_name","dosage_form","strength","manufacturer","gns","drug_text"]:
-    if c not in DF.columns: DF[c] = ""
-if "drug_text" not in DF.columns or DF["drug_text"].str.len().sum()==0:
-    DF["drug_text"] = DF.apply(_build_text, axis=1)
-
-DF = DF.reset_index(drop=True)
+    DF = pd.read_csv("drug_database.csv")
+except:
+    DF = pd.read_csv("merged_pharma_dataset.csv")
+
+DF = DF.fillna("")
+
+TEXT_COL = "drug_text" if "drug_text" in DF.columns else "generic_name"
+
+# -----------------------------
+# LOAD TFIDF MODEL
+# -----------------------------
+
+try:
+    VEC = joblib.load("tfidf_vectorizer.pkl")
+    MAT = joblib.load("tfidf_matrix.pkl")
+except:
+    VEC = TfidfVectorizer(max_features=15000, ngram_range=(1,2))
+    MAT = VEC.fit_transform(DF[TEXT_COL])
+
+# -----------------------------
+# COLOR SYSTEM
+# -----------------------------
+
+COLORS = {
+    "Allopathic":"#2563EB",
+    "Ayurvedic":"#16A34A",
+    "Unani":"#F59E0B",
+    "Homeopathic":"#7C3AED",
+    "Herbal":"#EF4444"
+}
 
 SYSTEMS = ["All Systems","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]
-SC = {"Allopathic":"#3B82F6","Ayurvedic":"#10B981",
-      "Unani":"#F59E0B","Homeopathic":"#8B5CF6","Herbal":"#EF4444"}
-EMOJI = {"Allopathic":"💊","Ayurvedic":"🌿","Unani":"☘️","Homeopathic":"💧","Herbal":"🌱"}
-
-# Pre-compute for analytics
-_SYS_VC  = DF["medical_system"].value_counts()
-_DOS_VC  = DF["dosage_form"].value_counts()
-_MFR_VC  = DF["manufacturer"].value_counts()
-_SYS_MFR = DF.groupby("medical_system")["manufacturer"].nunique()
-_FEAT    = np.array(VEC.get_feature_names_out())
-
-# ────────────────────────────────────────────────────────────────
-# 1. RETRIEVAL HELPERS
-# ────────────────────────────────────────────────────────────────
-
-def _encode(q):
-    q2 = re.sub(r"[^a-z0-9\s\+\-\./]"," ",q.lower())
-    return VEC.transform([re.sub(r"\s+"," ",q2).strip()])
-
-def _recommend(query, system, top_n, min_s):
-    sims = cosine_similarity(_encode(query), MAT).flatten()
-    if system not in ("All Systems","All",""):
+
+# ==========================================================
+# SMART SEARCH
+# ==========================================================
+
+def search_drug(query, system, topn):
+
+    if query.strip()=="":
+        return "<div class='ph'>🔍 Enter a drug name, symptom, or compound</div>", None
+
+    q_vec = VEC.transform([query])
+    sims = cosine_similarity(q_vec, MAT).flatten()
+
+    if system!="All Systems":
         mask = DF["medical_system"]==system
         sims[~mask.values]=0
-    idx=[i for i in sims.argsort()[-(top_n*4):][::-1] if sims[i]>=min_s][:top_n]
-    if not idx: return pd.DataFrame()
-    r=DF.iloc[idx].copy(); r["score"]=[round(float(sims[i]),4) for i in idx]
-    return r.sort_values("score",ascending=False).reset_index(drop=True)
-
-def _cross(query, tps):
-    sims = cosine_similarity(_encode(query), MAT).flatten()
-    rows=[]
-    for sys in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]:
-        sc=sims.copy(); sc[~(DF["medical_system"]==sys).values]=0
-        for i in [i for i in sc.argsort()[-tps:][::-1] if sims[i]>0.01]:
-            d=DF.iloc[i].to_dict(); d["score"]=round(float(sims[i]),4); rows.append(d)
-    if not rows: return pd.DataFrame()
-    return (pd.DataFrame(rows)
-              .sort_values(["medical_system","score"],ascending=[True,False])
-              .reset_index(drop=True))
-
-# ────────────────────────────────────────────────────────────────
-# 2. TAB 1 — SMART DRUG SEARCH
-# ────────────────────────────────────────────────────────────────
-
-def tab1(query, system, top_n, min_s):
-    if not query.strip():
-        return '<div class="ph">🔍 Type a drug name, compound, or symptom above and press Search</div>', None, ""
-
-    r = _recommend(query, system, int(top_n), float(min_s))
-    if r.empty:
-        return f'<div class="ph">No results found for <b>{query}</b>. Try lowering the similarity threshold.</div>', None, ""
-
-    cards = f'<div class="rh">Found <b>{len(r)}</b> results for "<b>{query}</b>"</div><div class="grid">'
-    for _, row in r.iterrows():
-        sys  = str(row.get("medical_system",""))
-        c    = SC.get(sys,"#6B7280")
-        em   = EMOJI.get(sys,"💊")
-        bn   = str(row.get("brand_name","—"))
-        gn   = str(row.get("gns","")) or str(row.get("generic_name","—"))
-        dos  = str(row.get("dosage_form","—"))
-        mfr  = str(row.get("manufacturer","—"))[:38]
-        sc_v = float(row.get("score",0))
-        pct  = int(sc_v*100)
+
+    idx = sims.argsort()[::-1][:topn]
+
+    results = DF.iloc[idx].copy()
+    results["score"] = sims[idx]
+
+    cards = "<div class='grid'>"
+
+    for _,r in results.iterrows():
+
+        sys = r.get("medical_system","Unknown")
+        col = COLORS.get(sys,"#64748B")
+
         cards += f"""
-        <div class="card" style="border-left:4px solid {c}">
-          <div class="ch">
-            <span class="sbadge" style="background:{c}18;color:{c};border:1px solid {c}35">{em} {sys}</span>
-            <span class="spct"  style="background:{c}12;color:{c}">{pct}%</span>
-          </div>
-          <div class="bn">{bn}</div>
-          <div class="gn">{gn[:70]+'…' if len(gn)>70 else gn}</div>
-          <div class="meta">💊 {dos} &nbsp;·&nbsp; 🏭 {mfr}</div>
-          <div class="bar"><div class="fill" style="width:{pct}%;background:{c}"></div></div>
-        </div>"""
+        <div class='card'>
+            <div class='badge' style='background:{col}20;color:{col}'>{sys}</div>
+            <div class='bn'>{r.get('brand_name','Unknown')}</div>
+            <div class='gn'>{r.get('generic_name','')}</div>
+            <div class='meta'>💊 {r.get('dosage_form','')} • 🏭 {r.get('manufacturer','')}</div>
+            <div class='score'>{round(r['score']*100,1)}% similarity</div>
+        </div>
+        """
+
     cards += "</div>"
 
     fig = px.bar(
-        r.head(15), x="score", y="brand_name", color="medical_system",
-        color_discrete_map=SC, orientation="h",
-        labels={"score":"Similarity Score","brand_name":""},
-        title=f'Similarity Scores — "{query}"',
+        results.head(15),
+        x="score",
+        y="brand_name",
+        color="medical_system",
+        orientation="h",
+        title="Similarity Scores"
     )
-    fig.update_layout(
-        height=max(340,len(r.head(15))*30+90),
-        paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
-        font=dict(family="Inter,sans-serif",size=11),
-        legend=dict(orientation="h",yanchor="bottom",y=1.02,title=None),
-        margin=dict(l=0,r=10,t=50,b=10), yaxis=dict(autorange="reversed"),
-        xaxis=dict(range=[0,1],gridcolor="#f1f5f9"),
+
+    fig.update_layout(height=420)
+
+    return cards, fig
+
+# ==========================================================
+# CROSS SYSTEM COMPARISON
+# ==========================================================
+
+def cross_compare(query):
+
+    if query.strip()=="":
+        return None
+
+    q_vec = VEC.transform([query])
+    sims = cosine_similarity(q_vec, MAT).flatten()
+
+    data=[]
+
+    for sys in COLORS:
+
+        mask = DF["medical_system"]==sys
+        scores = sims.copy()
+        scores[~mask.values]=0
+
+        idx = scores.argsort()[::-1][:3]
+
+        for i in idx:
+            data.append({
+                "system":sys,
+                "drug":DF.iloc[i].get("brand_name",""),
+                "score":scores[i]
+            })
+
+    df=pd.DataFrame(data)
+
+    fig=px.bar(
+        df,
+        x="system",
+        y="score",
+        color="system",
+        hover_name="drug",
+        title="Cross Medical System Comparison"
     )
-    dist = r["medical_system"].value_counts().to_dict()
-    stat = "  ·  ".join(f"**{k}** {v}" for k,v in dist.items())
-    return cards, fig, f"📊 {stat}"
-
-
-# ────────────────────────────────────────────────────────────────
-# 3. TAB 2 — CROSS-SYSTEM COMPARE
-# ────────────────────────────────────────────────────────────────
-
-def tab2(query, tps):
-    if not query.strip():
-        return '<div class="ph">Enter a query to compare drugs across all 5 medical traditions</div>', None
-
-    r = _cross(query, int(tps))
-    if r.empty:
-        return '<div class="ph">No cross-system results found.</div>', None
-
-    html = f'<div class="cph">Cross-system view for <b>"{query}"</b></div><div class="cgrid">'
-    for sys in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]:
-        sub = r[r["medical_system"]==sys]
-        c   = SC[sys]; em=EMOJI[sys]
-        html += f'<div class="scol" style="border-top:3px solid {c}"><div class="stitle" style="color:{c}">{em} {sys}</div>'
-        if sub.empty:
-            html += '<div class="nr">No match found</div>'
-        else:
-            for _,row in sub.iterrows():
-                bn  = str(row.get("brand_name","—"))
-                gn  = str(row.get("gns","")) or str(row.get("generic_name","—"))
-                dos = str(row.get("dosage_form","—"))
-                sc_ = int(float(row.get("score",0))*100)
-                html += f"""<div class="cc" style="border-left:3px solid {c}38">
-                  <div class="cbn">{bn}</div>
-                  <div class="cgn">{gn[:48]+'…' if len(gn)>48 else gn}</div>
-                  <div class="cm">{dos} · {sc_}%</div>
-                  <div class="sbar"><div class="sfill" style="width:{sc_}%;background:{c}"></div></div>
-                </div>"""
-        html += "</div>"
-    html += "</div>"
-
-    # Radar chart
-    avgs={s: float(r[r["medical_system"]==s]["score"].mean()) if not r[r["medical_system"]==s].empty else 0
-          for s in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]}
-    cats=list(avgs.keys()); vals=list(avgs.values())
-    fig=go.Figure(go.Scatterpolar(
-        r=vals+[vals[0]], theta=cats+[cats[0]], fill="toself",
-        fillcolor="rgba(59,130,246,0.12)", line=dict(color="#3B82F6",width=2.5),
-        marker=dict(size=9,color=[SC[s] for s in cats]+[SC[cats[0]]]),
-    ))
-    fig.update_layout(
-        polar=dict(radialaxis=dict(visible=True,range=[0,1],gridcolor="#e5e7eb"),
-                   angularaxis=dict(gridcolor="#e5e7eb",tickfont=dict(size=12))),
-        title=dict(text=f'Cross-System Radar — "{query}"',font=dict(size=13,color="#1e293b")),
-        paper_bgcolor="rgba(0,0,0,0)", font=dict(family="Inter,sans-serif"),
-        height=380, showlegend=False, margin=dict(l=50,r=50,t=60,b=30),
+
+    return fig
+
+# ==========================================================
+# DRUG FINGERPRINT
+# ==========================================================
+
+def fingerprint(drug):
+
+    if drug.strip()=="":
+        return "<div class='ph'>Enter a drug name</div>",None
+
+    q_vec = VEC.transform([drug])
+    sims = cosine_similarity(q_vec, MAT).flatten()
+
+    idx = sims.argmax()
+
+    row = DF.iloc[idx]
+
+    html=f"""
+    <div class='profile'>
+        <h2>{row.get('brand_name','')}</h2>
+        <p><b>Medical System:</b> {row.get('medical_system','')}</p>
+        <p><b>Generic:</b> {row.get('generic_name','')}</p>
+        <p><b>Dosage Form:</b> {row.get('dosage_form','')}</p>
+        <p><b>Manufacturer:</b> {row.get('manufacturer','')}</p>
+    </div>
+    """
+
+    vec = MAT[idx].toarray().flatten()
+    top = vec.argsort()[-20:]
+
+    fig = go.Figure()
+
+    fig.add_bar(
+        x=vec[top],
+        y=VEC.get_feature_names_out()[top],
+        orientation="h"
     )
+
+    fig.update_layout(title="Drug Feature Fingerprint",height=420)
+
     return html, fig
 
+# ==========================================================
+# DATASET ANALYTICS
+# ==========================================================
 
-# ────────────────────────────────────────────────────────────────
-# 4. TAB 3 — DATASET ANALYTICS  (3 sub-views)
-# ────────────────────────────────────────────────────────────────
-
-def _overview_fig():
-    fig=make_subplots(rows=2,cols=3,
-        subplot_titles=["System Share","Top 12 Dosage Forms","Manufacturers per System",
-                        "Top 15 Manufacturers","System × Dosage Heatmap","TF-IDF Vocab Share"],
-        specs=[[{"type":"domain"},{"type":"xy"},{"type":"xy"}],
-               [{"type":"xy"},{"type":"xy"},{"type":"domain"}]],
-        vertical_spacing=0.14,horizontal_spacing=0.08)
-
-    # 1 donut
-    fig.add_trace(go.Pie(
-        labels=_SYS_VC.index.tolist(),values=_SYS_VC.values.tolist(),hole=0.55,
-        marker=dict(colors=[SC.get(s,"#aaa") for s in _SYS_VC.index],
-                    line=dict(color="white",width=2.5)),
-        textinfo="label+percent",textfont=dict(size=10),showlegend=False,
-    ),row=1,col=1)
-
-    # 2 dosage bar
-    td=_DOS_VC.head(12)
-    fig.add_trace(go.Bar(
-        x=td.values[::-1],y=td.index[::-1].tolist(),orientation="h",
-        marker=dict(color=px.colors.sequential.Blues_r[:12],line=dict(color="white",width=1)),
-        text=[f"{v:,}" for v in td.values[::-1]],textposition="outside",showlegend=False,
-    ),row=1,col=2)
-
-    # 3 mfr per system
-    fig.add_trace(go.Bar(
-        x=_SYS_MFR.index.tolist(),y=_SYS_MFR.values.tolist(),
-        marker=dict(color=[SC.get(s,"#aaa") for s in _SYS_MFR.index],
-                    line=dict(color="white",width=2)),
-        text=_SYS_MFR.values.tolist(),textposition="outside",showlegend=False,
-    ),row=1,col=3)
-
-    # 4 top 15 mfr
-    tm=_MFR_VC.head(15)
-    fig.add_trace(go.Bar(
-        y=[m[:28] for m in tm.index[::-1].tolist()],x=tm.values[::-1].tolist(),
-        orientation="h",
-        marker=dict(color=tm.values[::-1].tolist(),colorscale="Viridis",
-                    showscale=False,line=dict(color="white",width=1)),
-        showlegend=False,
-    ),row=2,col=1)
-
-    # 5 heatmap
-    top8=_DOS_VC.head(8).index.tolist()
-    sysl=["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]
-    piv=pd.crosstab(DF["medical_system"],DF["dosage_form"])
-    z=[[int(piv[d].get(s,0)) if d in piv.columns else 0 for d in top8] for s in sysl]
-    fig.add_trace(go.Heatmap(
-        z=z,x=[d[:12] for d in top8],y=sysl,colorscale="YlOrRd",
-        text=z,texttemplate="%{text}",textfont=dict(size=9),
-        showscale=True,colorbar=dict(thickness=10,x=0.65,len=0.42),
-    ),row=2,col=2)
-
-    # 6 vocab share
-    vtoks={s:int((np.asarray(MAT[(DF["medical_system"]==s).values].mean(axis=0)).flatten()>0.001).sum())
-           for s in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]}
-    fig.add_trace(go.Pie(
-        labels=list(vtoks.keys()),values=list(vtoks.values()),hole=0.5,
-        marker=dict(colors=[SC.get(s,"#aaa") for s in vtoks],
-                    line=dict(color="white",width=2)),
-        textinfo="label+value",textfont=dict(size=10),showlegend=False,
-    ),row=2,col=3)
-
-    fig.update_layout(
-        height=720,paper_bgcolor="rgba(0,0,0,0)",plot_bgcolor="rgba(0,0,0,0)",
-        font=dict(family="Inter,sans-serif",size=11),
-        title=dict(text="PharmaBridge — Dataset Intelligence Dashboard",
-                   font=dict(size=16,color="#1e293b"),x=0.5),
-        margin=dict(l=10,r=10,t=80,b=10),
-    )
-    fig.update_xaxes(showgrid=True,gridcolor="#f1f5f9",zeroline=False)
-    fig.update_yaxes(showgrid=False)
-    return fig
+def dataset_dashboard():
 
-def _deep_fig(sel):
-    sub = DF if sel=="All" else DF[DF["medical_system"]==sel]
-    c   = SC.get(sel,"#3B82F6")
-    fig=make_subplots(rows=2,cols=2,
-        subplot_titles=[f"Top 20 Compounds ({sel})","Dosage Form Split",
-                        "Top 10 Manufacturers","Brand Count Comparison"],
-        specs=[[{"type":"xy"},{"type":"domain"}],[{"type":"xy"},{"type":"xy"}]],
-        vertical_spacing=0.16,horizontal_spacing=0.10)
-
-    # compound
-    if sel=="Homeopathic":   comp=sub["brand_name"].value_counts().head(20)
-    elif sel=="Allopathic":  comp=sub["generic_name"].dropna().value_counts().head(20)
-    else:                    comp=sub["gns"].dropna().value_counts().head(20)
-    fig.add_trace(go.Bar(
-        x=comp.values[::-1].tolist(),y=comp.index[::-1].tolist(),orientation="h",
-        marker=dict(color=c,opacity=0.85,line=dict(color="white",width=1)),
-        text=comp.values[::-1].tolist(),textposition="outside",showlegend=False,
-    ),row=1,col=1)
-
-    # dosage donut
-    dos=sub["dosage_form"].value_counts().head(8)
-    fig.add_trace(go.Pie(
-        labels=dos.index.tolist(),values=dos.values.tolist(),hole=0.48,
-        marker=dict(colors=px.colors.qualitative.Set3[:len(dos)],
-                    line=dict(color="white",width=2)),
-        textinfo="label+percent",textfont=dict(size=10),showlegend=False,
-    ),row=1,col=2)
-
-    # top mfr
-    mf=sub["manufacturer"].value_counts().head(10)
-    fig.add_trace(go.Bar(
-        x=mf.values[::-1].tolist(),y=[m[:26] for m in mf.index[::-1].tolist()],
-        orientation="h",
-        marker=dict(color=mf.values[::-1].tolist(),colorscale="Blues",
-                    showscale=False,line=dict(color="white",width=1)),
-        showlegend=False,
-    ),row=2,col=1)
-
-    # brand count
-    bc=DF.groupby("medical_system")["brand_name"].nunique().sort_values(ascending=False)
-    fig.add_trace(go.Bar(
-        x=bc.index.tolist(),y=bc.values.tolist(),
-        marker=dict(color=[c if s==sel else "#cbd5e1" for s in bc.index],
-                    line=dict(color="white",width=2)),
-        text=bc.values.tolist(),textposition="outside",showlegend=False,
-    ),row=2,col=2)
-
-    fig.update_layout(
-        height=680,paper_bgcolor="rgba(0,0,0,0)",plot_bgcolor="rgba(0,0,0,0)",
-        font=dict(family="Inter,sans-serif",size=11),
-        title=dict(text=f"Deep Dive: {sel}",font=dict(size=15,color="#1e293b"),x=0.5),
-        margin=dict(l=10,r=10,t=70,b=10),
+    sys = DF["medical_system"].value_counts()
+
+    fig = px.pie(
+        names=sys.index,
+        values=sys.values,
+        title="Drug Distribution Across Medical Systems"
     )
-    fig.update_xaxes(showgrid=True,gridcolor="#f1f5f9",zeroline=False)
-    fig.update_yaxes(showgrid=False)
-    return fig
 
-def _treemap_fig():
-    samp=DF.groupby(["medical_system","dosage_form"]).size().reset_index(name="count")
-    samp=samp[samp["count"]>=5]
-    fig=px.treemap(samp,path=["medical_system","dosage_form"],values="count",
-                   color="medical_system",color_discrete_map=SC,
-                   title="Drug Hierarchy: Medical System → Dosage Form")
-    fig.update_traces(textinfo="label+value+percent parent",textfont=dict(size=12))
-    fig.update_layout(height=520,paper_bgcolor="rgba(0,0,0,0)",
-                      font=dict(family="Inter,sans-serif",size=12),
-                      title=dict(font=dict(size=15,color="#1e293b"),x=0.5),
-                      margin=dict(l=10,r=10,t=60,b=10))
+    fig.update_layout(height=500)
+
     return fig
 
-def tab3_deep_update(sel):
-    return _deep_fig(sel)
-
-
-# ────────────────────────────────────────────────────────────────
-# 5. TAB 4 — DRUG FINGERPRINT  (single drug profile)
-# ────────────────────────────────────────────────────────────────
-
-def tab4_fingerprint(brand_query):
-    """Search for a specific drug and show a rich visual profile card + radar of its TF-IDF feature weights."""
-    if not brand_query.strip():
-        return '<div class="ph">Enter a brand name to see its full drug profile</div>', None
-
-    # Find best match
-    sims = cosine_similarity(_encode(brand_query), MAT).flatten()
-    idx  = int(sims.argsort()[-1])
-    row  = DF.iloc[idx]
-    sc_v = float(sims[idx])
-
-    if sc_v < 0.01:
-        return f'<div class="ph">No drug found matching "<b>{brand_query}</b>".</div>', None
-
-    sys_n = str(row.get("medical_system",""))
-    c     = SC.get(sys_n,"#6B7280")
-    em    = EMOJI.get(sys_n,"💊")
-    bn    = str(row.get("brand_name","—"))
-    gn    = str(row.get("gns","")) or str(row.get("generic_name","—"))
-    dos   = str(row.get("dosage_form","—"))
-    mfr   = str(row.get("manufacturer","—"))
-    clu   = str(row.get("cluster","—"))
-    dart  = str(row.get("DAR","—")) if "DAR" in row.index else "—"
-    txt   = str(row.get("drug_text",""))
-
-    # Siblings (same gns/cluster)
-    sib_mask = (DF["medical_system"]==sys_n) & (DF["gns"]==str(row.get("gns","")))
-    sib_count = sib_mask.sum()-1
-
-    html = f"""
-    <div class="fp-card" style="border:2px solid {c}40;background:white;border-radius:16px;overflow:hidden">
-      <div class="fp-banner" style="background:linear-gradient(135deg,{c},{c}99);padding:20px 24px;color:white">
-        <div style="font-size:0.85rem;opacity:0.85;margin-bottom:4px">{em} {sys_n}</div>
-        <div style="font-size:1.7rem;font-weight:800;letter-spacing:-0.5px">{bn}</div>
-        <div style="font-size:0.95rem;opacity:0.9;margin-top:4px">{gn[:80]}</div>
-        <div style="margin-top:12px;background:rgba(255,255,255,0.2);border-radius:20px;padding:5px 14px;
-                    display:inline-block;font-size:0.8rem;font-weight:600">
-            {int(sc_v*100)}% match confidence
-        </div>
-      </div>
-      <div style="padding:20px 24px;display:grid;grid-template-columns:1fr 1fr;gap:14px">
-        <div class="fp-row"><span class="fp-k">💊 Dosage Form</span><span class="fp-v">{dos}</span></div>
-        <div class="fp-row"><span class="fp-k">🏭 Manufacturer</span><span class="fp-v">{mfr[:40]}</span></div>
-        <div class="fp-row"><span class="fp-k">🧬 Medical System</span><span class="fp-v">{sys_n}</span></div>
-        <div class="fp-row"><span class="fp-k">📂 Cluster</span><span class="fp-v">#{clu}</span></div>
-        <div class="fp-row"><span class="fp-k">📋 DAR Number</span><span class="fp-v">{dart}</span></div>
-        <div class="fp-row"><span class="fp-k">👥 Same-compound drugs</span><span class="fp-v">{sib_count}</span></div>
-      </div>
-      <div style="padding:0 24px 20px;font-size:0.82rem;color:#64748b">
-        <b>Drug Text (TF-IDF input):</b> <code style="background:#f1f5f9;padding:3px 8px;border-radius:6px">{txt[:120]}</code>
-      </div>
-    </div>"""
-
-    # Top TF-IDF features for this drug
-    vec_row = MAT[idx]
-    feat_idx = np.asarray(vec_row.todense()).flatten().argsort()[-20:][::-1]
-    feat_scores = np.asarray(vec_row.todense()).flatten()[feat_idx]
-    feat_labels = _FEAT[feat_idx]
-    mask = feat_scores > 0
-    feat_labels = feat_labels[mask]; feat_scores = feat_scores[mask]
-
-    fig = go.Figure(go.Bar(
-        x=feat_scores[::-1], y=feat_labels[::-1],
-        orientation="h",
-        marker=dict(
-            color=feat_scores[::-1],
-            colorscale=[[0,"#dbeafe"],[1,c]],
-            showscale=False,
-            line=dict(color="white",width=1),
-        ),
-        text=[f"{v:.3f}" for v in feat_scores[::-1]],
-        textposition="outside",
-    ))
-    fig.update_layout(
-        title=dict(text=f"TF-IDF Feature Fingerprint: {bn}",
-                   font=dict(size=13,color="#1e293b")),
-        height=max(300, len(feat_labels)*28+80),
-        paper_bgcolor="rgba(0,0,0,0)",plot_bgcolor="rgba(0,0,0,0)",
-        font=dict(family="Inter,sans-serif",size=11),
-        margin=dict(l=10,r=60,t=50,b=10),
-        xaxis=dict(gridcolor="#f1f5f9",title="TF-IDF Weight"),
-        yaxis=dict(title=""),
-    )
-    return html, fig
+# ==========================================================
+# DRUG EXPLORER
+# ==========================================================
 
+def explorer(system, search):
 
-# ────────────────────────────────────────────────────────────────
-# 6. TAB 5 — FDA LIVE INTELLIGENCE
-# ────────────────────────────────────────────────────────────────
+    sub = DF.copy()
 
-FDA_NAME_MAP={
-    "Paracetamol":"acetaminophen","Azithromycin":"azithromycin",
-    "Ciprofloxacin":"ciprofloxacin","Amoxicillin":"amoxicillin",
-    "Omeprazole":"omeprazole","Metformin":"metformin",
-    "Atorvastatin":"atorvastatin","Amlodipine":"amlodipine",
-    "Ceftriaxone":"ceftriaxone","Diclofenac":"diclofenac sodium",
-    "Esomeprazole":"esomeprazole","Cefixime":"cefixime",
-    "Salbutamol":"albuterol","Ibuprofen":"ibuprofen",
-    "Metronidazole":"metronidazole","Cefuroxime":"cefuroxime",
-}
+    if system!="All":
+        sub=sub[sub["medical_system"]==system]
 
-def _fda_fetch(drug, endpoint):
-    term=FDA_NAME_MAP.get(drug,drug.lower())
-    base=f"https://api.fda.gov/drug/{endpoint}.json"
-    for field in [f"openfda.generic_name:{term}",f"openfda.brand_name:{term}"]:
-        try:
-            r=requests.get(base,params={"search":field,"limit":"3"},timeout=9)
-            if r.status_code==200:
-                res=r.json().get("results",[])
-                if res: return res, term
-        except: pass
-    return [], term
-
-def tab5_fda(drug, ep_label):
-    if not drug.strip():
-        return '<div class="ph">🏥 Enter a drug name to fetch live FDA data</div>'
-    ep_map={"Drug Labels":"label","Adverse Events (FAERS)":"event","NDC Directory":"ndc"}
-    ep=ep_map.get(ep_label,"label")
-    results,term=_fda_fetch(drug,ep)
-
-    if not results:
-        return f"""<div class="fda-miss">
-          <div style="font-size:2.5rem;margin-bottom:12px">🔍</div>
-          <div><b>No FDA data found for "{drug}"</b></div>
-          <div style="color:#64748b;font-size:0.88rem;margin-top:8px;line-height:1.7">
-            This drug may not be in the US FDA database (common for Bangladesh-registry drugs).<br>
-            <b>Try:</b> Paracetamol · Azithromycin · Ciprofloxacin · Omeprazole · Metformin · Ibuprofen
-          </div></div>"""
-
-    html=f"""<div class="fda-hdr">
-      <span class="fda-badge">🇺🇸 FDA {ep_label}</span>
-      <b>{drug}</b> → searched as <code>{term}</code>
-      <span class="fda-cnt">{len(results)} record(s)</span>
-    </div>"""
-
-    if ep=="label":
-        for i,res in enumerate(results[:3],1):
-            o=res.get("openfda",{})
-            brand=", ".join(o.get("brand_name",["—"])[:2])
-            gen  =", ".join(o.get("generic_name",["—"])[:2])
-            mfr  =", ".join(o.get("manufacturer_name",["—"])[:1])
-            purp =str(res.get("purpose",["—"])[0])[:280] if res.get("purpose") else "—"
-            ind  =str(res.get("indications_and_usage",["—"])[0])[:380] if res.get("indications_and_usage") else "—"
-            warn =str(res.get("warnings",["—"])[0])[:280] if res.get("warnings") else "—"
-            html+=f"""<div class="fda-card">
-              <div class="fda-num">📄 Record {i}</div>
-              <table class="fda-tbl">
-                <tr><td class="fk">Brand Name</td><td>{brand}</td></tr>
-                <tr><td class="fk">Generic Name</td><td>{gen}</td></tr>
-                <tr><td class="fk">Manufacturer</td><td>{mfr}</td></tr>
-                <tr><td class="fk">Purpose</td><td>{purp}</td></tr>
-                <tr><td class="fk">Indications</td><td>{ind}</td></tr>
-                <tr><td class="fk">Warnings</td><td>{warn}</td></tr>
-              </table></div>"""
-
-    elif ep=="event":
-        for i,res in enumerate(results[:3],1):
-            pt=res.get("patient",{})
-            rxn=", ".join(r.get("reactionmeddrapt","") for r in pt.get("reaction",[])[:6])
-            drg=", ".join(d.get("medicinalproduct","") for d in pt.get("drug",[])[:4])
-            sev="⚠️ Serious" if res.get("serious")=="1" else "ℹ️ Non-Serious"
-            html+=f"""<div class="fda-card">
-              <div class="fda-num">Event {i} — {sev}</div>
-              <table class="fda-tbl">
-                <tr><td class="fk">Reactions</td><td>{rxn or '—'}</td></tr>
-                <tr><td class="fk">Drugs Involved</td><td>{drg or '—'}</td></tr>
-              </table></div>"""
-
-    elif ep=="ndc":
-        for i,res in enumerate(results[:3],1):
-            html+=f"""<div class="fda-card">
-              <div class="fda-num">NDC {i}</div>
-              <table class="fda-tbl">
-                <tr><td class="fk">NDC Code</td><td>{res.get('product_ndc','—')}</td></tr>
-                <tr><td class="fk">Brand</td><td>{res.get('brand_name','—')}</td></tr>
-                <tr><td class="fk">Generic</td><td>{res.get('generic_name','—')}</td></tr>
-                <tr><td class="fk">Dosage Form</td><td>{res.get('dosage_form','—')}</td></tr>
-                <tr><td class="fk">Route</td><td>{res.get('route','—')}</td></tr>
-                <tr><td class="fk">Labeler</td><td>{res.get('labeler_name','—')}</td></tr>
-              </table></div>"""
-    return html
-
-
-# ────────────────────────────────────────────────────────────────
-# 7. TAB 6 — AI MEDICAL Q&A  (HuggingFace Inference API)
-# ────────────────────────────────────────────────────────────────
-
-SYS_PROMPT=(
-    "You are PharmaBridge AI — a knowledgeable, friendly pharmaceutical assistant. "
-    "You help healthcare professionals and students understand drug information, "
-    "pharmacology, traditional medicine (Ayurvedic, Unani, Homeopathic, Herbal), "
-    "drug interactions, and the Bangladesh drug registry. "
-    "Be concise, accurate, and always note that answers are educational, "
-    "not a substitute for professional medical advice."
-)
-
-HF_MODELS=[
-    "mistralai/Mistral-7B-Instruct-v0.3",
-    "HuggingFaceH4/zephyr-7b-beta",
-    "google/flan-t5-xxl",
-]
-
-def tab6_ai(question, history):
-    if not question.strip():
-        return history, ""
-    history=history or []
-
-    prompt=f"<s>[INST] {SYS_PROMPT}\n\nQuestion: {question} [/INST]"
-    headers={"Content-Type":"application/json"}
-    answer=""
-
-    for model_url in [f"https://api-inference.huggingface.co/models/{m}" for m in HF_MODELS]:
-        payload={
-            "inputs": prompt,
-            "parameters":{"max_new_tokens":500,"temperature":0.65,
-                           "top_p":0.9,"repetition_penalty":1.1,
-                           "return_full_text":False},
-        }
-        # flan-t5 uses different format
-        if "flan" in model_url:
-            payload={"inputs":f"As a pharmacist, answer clearly: {question}",
-                     "parameters":{"max_new_tokens":350}}
-        try:
-            r=requests.post(model_url,headers=headers,json=payload,timeout=28)
-            if r.status_code==200:
-                d=r.json()
-                txt=(d[0].get("generated_text","") if isinstance(d,list) else d.get("generated_text","")).strip()
-                if len(txt)>30:
-                    answer=txt; break
-        except: continue
-
-    if not answer:
-        answer=(
-            "⚠️ The AI model is warming up (HuggingFace free tier cold-start). "
-            "Please wait ~20 seconds and try again.\n\n"
-            "**Meanwhile**, you can:\n"
-            "- Use the **Smart Search** tab to look up this drug directly\n"
-            "- Use the **FDA Live Data** tab for official drug information"
-        )
-
-    history.append((question, answer))
-    return history, ""
-
-def tab6_clear():
-    return [], ""
-
-
-# ────────────────────────────────────────────────────────────────
-# 8. TAB 7 — DRUG EXPLORER  (browse & filter)
-# ────────────────────────────────────────────────────────────────
-
-_ALL_DOS=["All"]+sorted(DF["dosage_form"].dropna().unique().tolist())
-
-def _dos_choices(sys):
-    if sys=="All":
-        return gr.update(choices=_ALL_DOS, value="All")
-    opts=["All"]+sorted(DF[DF["medical_system"]==sys]["dosage_form"].dropna().unique().tolist())
-    return gr.update(choices=opts, value="All")
-
-def tab7_explore(system, dosage, search, page):
-    sub=DF.copy()
-    if system!="All": sub=sub[sub["medical_system"]==system]
-    if dosage !="All": sub=sub[sub["dosage_form"]==dosage]
-    if search.strip():
-        t=search.lower().strip()
-        sub=sub[sub["brand_name"].str.lower().str.contains(t,na=False)|
-                sub["gns"].str.lower().str.contains(t,na=False)|
-                sub["generic_name"].str.lower().str.contains(t,na=False)|
-                sub["manufacturer"].str.lower().str.contains(t,na=False)]
-
-    total=len(sub); PG=20
-    page=max(1,int(page)); maxp=max(1,(total+PG-1)//PG); page=min(page,maxp)
-    sl=sub.iloc[(page-1)*PG:page*PG]
-
-    if sl.empty:
-        return '<div class="ph">No records match your filters.</div>', "0 records"
+    if search:
+        s=search.lower()
+        sub=sub[sub["brand_name"].str.lower().str.contains(s)]
 
     rows=""
-    for _,row in sl.iterrows():
-        sys_n=str(row.get("medical_system",""))
-        c=SC.get(sys_n,"#6B7280"); em=EMOJI.get(sys_n,"💊")
-        bn=str(row.get("brand_name","—"))
-        gn=str(row.get("gns","")) or str(row.get("generic_name","—"))
-        dos_v=str(row.get("dosage_form","—"))
-        mfr=str(row.get("manufacturer","—"))[:36]
-        rows+=f"""<tr>
-          <td><b>{bn}</b></td>
-          <td style="max-width:190px">{gn[:46]}</td>
-          <td>{dos_v}</td>
-          <td><span class="sb2" style="background:{c}18;color:{c};border:1px solid {c}30">{em} {sys_n}</span></td>
-          <td style="color:#64748b">{mfr}</td>
-        </tr>"""
-
-    tbl=f"""<table class="xtbl">
-      <thead><tr><th>Brand Name</th><th>Compound / Identity</th>
-      <th>Dosage Form</th><th>System</th><th>Manufacturer</th></tr></thead>
-      <tbody>{rows}</tbody></table>"""
-
-    return tbl, f"Page **{page}** / {maxp}  ·  **{total:,}** records"
-
-
-# ────────────────────────────────────────────────────────────────
-# 9. CSS
-# ────────────────────────────────────────────────────────────────
+
+    for _,r in sub.head(100).iterrows():
+
+        rows+=f"""
+        <tr>
+        <td>{r.get('brand_name','')}</td>
+        <td>{r.get('generic_name','')}</td>
+        <td>{r.get('dosage_form','')}</td>
+        <td>{r.get('medical_system','')}</td>
+        </tr>
+        """
+
+    table=f"""
+    <table class='tbl'>
+    <tr>
+    <th>Brand</th>
+    <th>Generic</th>
+    <th>Dosage</th>
+    <th>System</th>
+    </tr>
+    {rows}
+    </table>
+    """
+
+    return table
+
+# ==========================================================
+# PREMIUM GLASS UI CSS
+# ==========================================================
 
 CSS="""
-@import url('https://fonts.googleapis.com/css2?family=Inter:ital,wght@0,300;0,400;0,500;0,600;0,700;0,800;1,400&display=swap');
-*{box-sizing:border-box}
-body,.gradio-container{font-family:'Inter',sans-serif!important;background:#f0f4f8!important}
-
-/* ── HEADER ─────────────────────────────────────────────── */
-.app-hdr{
-  background:linear-gradient(135deg,#0f172a 0%,#1e3a8a 45%,#0369a1 100%);
-  border-radius:18px;padding:28px 32px;margin-bottom:4px;color:#fff;
-  box-shadow:0 10px 40px rgba(30,58,138,.35);
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap');
+
+body{
+background:linear-gradient(135deg,#eef2ff,#f8fafc);
+font-family:'Inter';
 }
-.app-title{font-size:2.1rem;font-weight:800;letter-spacing:-1px;margin:0}
-.app-sub{font-size:1rem;opacity:.82;margin:6px 0 0}
-.hbadges{display:flex;gap:8px;margin-top:14px;flex-wrap:wrap}
-.hbadge{background:rgba(255,255,255,.16);border:1px solid rgba(255,255,255,.28);
-        border-radius:20px;padding:4px 13px;font-size:.78rem;font-weight:500}
-.stats-row{display:flex;gap:10px;margin-top:16px;flex-wrap:wrap}
-.stat{background:rgba(255,255,255,.12);border-radius:12px;padding:8px 16px;text-align:center;min-width:88px}
-.sn{font-size:1.45rem;font-weight:800;display:block}
-.sl{font-size:.7rem;opacity:.78;text-transform:uppercase;letter-spacing:.5px}
-
-/* ── TABS ────────────────────────────────────────────────── */
-.tab-nav button{font-weight:500!important;font-size:.88rem!important;border-radius:8px 8px 0 0!important}
-.tab-nav button.selected{color:#1d4ed8!important;border-bottom:3px solid #1d4ed8!important;font-weight:700!important}
-
-/* ── INPUTS ──────────────────────────────────────────────── */
-.gr-input,textarea,.gr-dropdown select{
-  border-radius:10px!important;border:1.5px solid #e2e8f0!important;
-  font-family:'Inter',sans-serif!important;transition:border-color .2s!important;
+
+.grid{
+display:grid;
+grid-template-columns:repeat(auto-fill,minmax(260px,1fr));
+gap:18px
 }
-.gr-input:focus,textarea:focus{border-color:#3b82f6!important;box-shadow:0 0 0 3px rgba(59,130,246,.1)!important}
-.gr-button-primary{
-  background:linear-gradient(135deg,#1d4ed8,#0891b2)!important;
-  border:none!important;border-radius:10px!important;font-weight:700!important;
-  letter-spacing:.2px!important;box-shadow:0 4px 14px rgba(29,78,216,.3)!important;
-  transition:transform .15s,box-shadow .15s!important;
+
+.card{
+background:rgba(255,255,255,0.85);
+backdrop-filter:blur(10px);
+padding:20px;
+border-radius:16px;
+box-shadow:0 10px 30px rgba(0,0,0,.08);
+transition:all .25s
+}
+
+.card:hover{
+transform:translateY(-4px);
+box-shadow:0 20px 40px rgba(0,0,0,.12)
+}
+
+.badge{
+font-size:12px;
+padding:4px 10px;
+border-radius:20px;
+display:inline-block;
+margin-bottom:6px
+}
+
+.bn{
+font-size:18px;
+font-weight:700
+}
+
+.gn{
+color:#475569;
+font-size:14px;
+margin-top:4px
+}
+
+.meta{
+font-size:13px;
+color:#64748B;
+margin-top:8px
+}
+
+.score{
+color:#2563EB;
+font-weight:600;
+margin-top:10px
+}
+
+.ph{
+text-align:center;
+padding:60px;
+background:white;
+border-radius:16px;
+color:#64748B
+}
+
+.profile{
+background:white;
+padding:25px;
+border-radius:16px;
+box-shadow:0 8px 24px rgba(0,0,0,.08)
+}
+
+.tbl{
+width:100%;
+border-collapse:collapse
+}
+
+.tbl th{
+background:#0f172a;
+color:white;
+padding:10px
+}
+
+.tbl td{
+padding:10px;
+border-bottom:1px solid #e2e8f0
 }
-.gr-button-primary:hover{transform:translateY(-1px)!important;box-shadow:0 6px 22px rgba(29,78,216,.4)!important}
-
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-/* ── CROSS COMPARE ───────────────────────────────────────── */
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-
-/* ── FDA ─────────────────────────────────────────────────── */
-.fda-hdr{background:linear-gradient(135deg,#eff6ff,#e0f2fe);border-radius:11px;
-         padding:14px 18px;margin-bottom:14px;display:flex;align-items:center;
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-
-/* ── CHATBOT ─────────────────────────────────────────────── */
-.chatbot{border-radius:13px!important;border:1.5px solid #e2e8f0!important}
-
-/* ── EXPLORER TABLE ──────────────────────────────────────── */
-.xtbl{width:100%;border-collapse:collapse;font-size:.83rem}
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-
-code{background:#f1f5f9;padding:2px 7px;border-radius:5px;font-size:.84em;color:#0891b2}
-"""
 
-# ────────────────────────────────────────────────────────────────
-# 10. BUILD GRADIO APP
-# ────────────────────────────────────────────────────────────────
-
-HEADER = f"""
-<div class="app-hdr">
-  <div class="app-title">💊 PharmaBridge</div>
-  <div class="app-sub">Cross-Medical-System Drug Intelligence Engine · Bangladesh National Drug Registry</div>
-  <div class="hbadges">
-    <span class="hbadge">🔬 TF-IDF + Cosine Similarity</span>
-    <span class="hbadge">🧠 SVD + K-Means Clustering</span>
-    <span class="hbadge">🌐 OpenFDA Live API</span>
-    <span class="hbadge">🤖 Mistral-7B AI Assistant</span>
-    <span class="hbadge">📊 Interactive Dashboards</span>
-  </div>
-  <div class="stats-row">
-    <div class="stat"><span class="sn">53,584</span><span class="sl">Total Drugs</span></div>
-    <div class="stat"><span class="sn">5</span><span class="sl">Med. Systems</span></div>
-    <div class="stat"><span class="sn">725</span><span class="sl">Manufacturers</span></div>
-    <div class="stat"><span class="sn">12,311</span><span class="sl">TF-IDF Features</span></div>
-    <div class="stat"><span class="sn">95.5%</span><span class="sl">Precision@10</span></div>
-    <div class="stat"><span class="sn">0.2159</span><span class="sl">Silhouette</span></div>
-  </div>
-</div>
 """
 
-with gr.Blocks(css=CSS, title="PharmaBridge", theme=gr.themes.Base(
-    primary_hue=gr.themes.colors.blue,
-    font=gr.themes.GoogleFont("Inter"),
-)) as app:
+# ==========================================================
+# UI
+# ==========================================================
 
-    gr.HTML(HEADER)
+with gr.Blocks(css=CSS,title="PharmaBridge") as app:
 
-    with gr.Tabs(elem_classes="tab-nav"):
+    gr.Markdown("""
+# 💊 PharmaBridge AI
+### Cross‑Medical‑System Drug Intelligence Platform
+""")
+
+    with gr.Tabs():
 
-        # ── TAB 1 ─────────────────────────────────────────────────
         with gr.Tab("🔍 Smart Search"):
-            with gr.Row(equal_height=True):
-                with gr.Column(scale=4):
-                    t1q = gr.Textbox(label="Search Query",
-                        placeholder="Try: Azithromycin, Ashwagandha, nux vomica, sharbat amrood, paracetamol fever…",
-                        lines=1)
-                with gr.Column(scale=1):
-                    t1sys = gr.Dropdown(choices=SYSTEMS, value="All Systems", label="System")
-                with gr.Column(scale=1):
-                    t1btn = gr.Button("🔍 Search", variant="primary", scale=1)
-            with gr.Row():
-                t1n   = gr.Slider(5,50,value=12,step=1,label="Max Results")
-                t1s   = gr.Slider(0.0,0.5,value=0.04,step=0.01,label="Min Similarity")
-            t1stat  = gr.Markdown("")
-            t1cards = gr.HTML('<div class="ph">🔍 Enter a drug name, compound, or symptom above</div>')
-            t1chart = gr.Plot(label="Score Distribution")
-
-            t1btn.click(tab1,[t1q,t1sys,t1n,t1s],[t1cards,t1chart,t1stat])
-            t1q.submit(tab1,[t1q,t1sys,t1n,t1s],[t1cards,t1chart,t1stat])
-
-            gr.Examples([
-                ["Azithromycin 500mg","Allopathic"],
-                ["Ashwagandha capsule","Ayurvedic"],
-                ["Nux Vomica liquid","Homeopathic"],
-                ["Sharbat Amrood","Unani"],
-                ["Moringa leaf powder","Herbal"],
-                ["antibiotic tablet","All Systems"],
-                ["digestive capsule","All Systems"],
-            ], inputs=[t1q,t1sys], label="Quick Examples")
-
-        # ── TAB 2 ─────────────────────────────────────────────────
-        with gr.Tab("⚖️ Cross-System Compare"):
-            with gr.Row(equal_height=True):
-                with gr.Column(scale=5):
-                    t2q = gr.Textbox(label="Query",
-                        placeholder="e.g. pain relief tablet, digestive liver, sleep anxiety, blood pressure…",
-                        lines=1)
-                with gr.Column(scale=1):
-                    t2n = gr.Slider(1,5,value=3,step=1,label="Results / System")
-                with gr.Column(scale=1):
-                    t2btn = gr.Button("⚖️ Compare", variant="primary")
-            t2cards = gr.HTML('<div class="ph">Compare the same therapeutic need across all 5 medical traditions simultaneously</div>')
-            t2radar = gr.Plot(label="Cross-System Similarity Radar")
-
-            t2btn.click(tab2,[t2q,t2n],[t2cards,t2radar])
-            t2q.submit(tab2,[t2q,t2n],[t2cards,t2radar])
-            gr.Examples([
-                ["digestive liver tablet"],["pain anti-inflammatory"],
-                ["antibiotic infection"],["blood pressure"],
-                ["cough respiratory"],["sleep anxiety stress"],
-            ], inputs=[t2q])
-
-        # ── TAB 3 ─────────────────────────────────────────────────
+
+            q=gr.Textbox(label="Search Drug")
+            sys=gr.Dropdown(SYSTEMS,value="All Systems")
+            n=gr.Slider(5,30,value=10)
+            btn=gr.Button("Search")
+
+            cards=gr.HTML()
+            chart=gr.Plot()
+
+            btn.click(search_drug,[q,sys,n],[cards,chart])
+
+        with gr.Tab("⚖️ Cross System Compare"):
+
+            q2=gr.Textbox(label="Query")
+            btn2=gr.Button("Compare")
+
+            fig=gr.Plot()
+
+            btn2.click(cross_compare,q2,fig)
+
         with gr.Tab("📊 Dataset Analytics"):
-            with gr.Tabs():
-                with gr.Tab("🌐 Overview Dashboard"):
-                    ov_btn = gr.Button("📊 Render Dashboard", variant="primary")
-                    ov_fig = gr.Plot()
-                    ov_btn.click(_overview_fig,[],[ov_fig])
-                    app.load(_overview_fig,[],[ov_fig])
-
-                with gr.Tab("🔎 System Deep Dive"):
-                    with gr.Row():
-                        dd_sys = gr.Dropdown(
-                            choices=["All","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"],
-                            value="Allopathic", label="Select System")
-                        dd_btn = gr.Button("Analyze", variant="primary")
-                    dd_fig = gr.Plot()
-                    dd_btn.click(_deep_fig,[dd_sys],[dd_fig])
-                    dd_sys.change(_deep_fig,[dd_sys],[dd_fig])
-                    app.load(lambda:_deep_fig("Allopathic"),[],[dd_fig])
-
-                with gr.Tab("🗺️ Treemap Explorer"):
-                    tm_btn = gr.Button("🗺️ Render Treemap", variant="primary")
-                    tm_fig = gr.Plot()
-                    tm_btn.click(_treemap_fig,[],[tm_fig])
-                    app.load(_treemap_fig,[],[tm_fig])
-
-        # ── TAB 4 ─────────────────────────────────────────────────
+
+            btn3=gr.Button("Load Dashboard")
+            dash=gr.Plot()
+
+            btn3.click(dataset_dashboard,outputs=dash)
+
         with gr.Tab("🧬 Drug Fingerprint"):
-            gr.Markdown("""
-            ### Single Drug Deep-Dive
-            Search for any drug to see its **full profile card** plus a bar chart of its
-            top TF-IDF feature weights — the exact tokens driving its similarity scores.
-            """)
-            with gr.Row(equal_height=True):
-                fp_q   = gr.Textbox(label="Brand Name or Compound",
-                    placeholder="e.g. Azithromycin, Ashwagandha, Nux Vomica, Sharbat Amrood…", lines=1)
-                fp_btn = gr.Button("🧬 Profile", variant="primary")
-            fp_card = gr.HTML('<div class="ph">🧬 Enter a drug or compound name to generate its fingerprint</div>')
-            fp_fig  = gr.Plot(label="TF-IDF Feature Fingerprint")
-
-            fp_btn.click(tab4_fingerprint,[fp_q],[fp_card,fp_fig])
-            fp_q.submit(tab4_fingerprint,[fp_q],[fp_card,fp_fig])
-            gr.Examples([
-                ["Azithromycin"],["Ashwagandha"],["Nux Vomica"],
-                ["Sharbat Amrood"],["Moringa"],["Paracetamol"],
-            ], inputs=[fp_q])
-
-        # ── TAB 5 ─────────────────────────────────────────────────
-        with gr.Tab("🏥 FDA Live Data"):
-            gr.Markdown("> **Live OpenFDA API** — US drug labels, adverse events (FAERS), and NDC records. "
-                        "~40% of Bangladesh registry drugs appear here. Bangladeshi names auto-mapped to FDA terms.")
-            with gr.Row(equal_height=True):
-                fda_drug = gr.Textbox(label="Drug Name",
-                    placeholder="Paracetamol, Azithromycin, Ciprofloxacin, Omeprazole, Metformin…", lines=1)
-                fda_ep   = gr.Radio(["Drug Labels","Adverse Events (FAERS)","NDC Directory"],
-                    value="Drug Labels", label="FDA Database")
-                fda_btn  = gr.Button("🔎 Fetch", variant="primary")
-            fda_out = gr.HTML('<div class="ph">🏥 Enter a drug name and click Fetch</div>')
-            fda_btn.click(tab5_fda,[fda_drug,fda_ep],[fda_out])
-            fda_drug.submit(tab5_fda,[fda_drug,fda_ep],[fda_out])
-            gr.Examples([["Paracetamol"],["Azithromycin"],["Ciprofloxacin"],
-                         ["Omeprazole"],["Metformin"],["Ibuprofen"]], inputs=[fda_drug])
-
-        # ── TAB 6 ─────────────────────────────────────────────────
-        with gr.Tab("🤖 AI Medical Q&A"):
-            gr.Markdown("""
-            ### PharmaBridge AI — Pharmaceutical Q&A
-            Powered by **Mistral-7B-Instruct** via HuggingFace Inference API (free, no key needed).
-            Ask anything about drugs, pharmacology, traditional medicine, or the Bangladesh registry.
-
-            > ⚠️ Educational only — not a substitute for professional medical advice. Model may take ~20s to cold-start.
-            """)
-            ai_bot   = gr.Chatbot(label="PharmaBridge AI", height=450, elem_classes="chatbot")
-            with gr.Row():
-                ai_inp   = gr.Textbox(label="Your Question", lines=2, scale=5,
-                    placeholder="e.g. What is Ashwagandha used for? / Side effects of Azithromycin? / What is Unani medicine?")
-                with gr.Column(scale=1):
-                    ai_send  = gr.Button("Send 💬", variant="primary")
-                    ai_clear = gr.Button("Clear 🗑️")
-            ai_send.click(tab6_ai,[ai_inp,ai_bot],[ai_bot,ai_inp])
-            ai_inp.submit(tab6_ai,[ai_inp,ai_bot],[ai_bot,ai_inp])
-            ai_clear.click(tab6_clear,[],[ai_bot,ai_inp])
-            gr.Examples([
-                ["What is Ashwagandha used for in Ayurvedic medicine?"],
-                ["Explain Unani medicine and its traditional formulations"],
-                ["What are the common side effects of Azithromycin?"],
-                ["How does TF-IDF cosine similarity work for drug retrieval?"],
-                ["What is Homeopathic potency and how are remedies prepared?"],
-                ["Compare Allopathic and Herbal medicine approaches"],
-            ], inputs=[ai_inp])
-
-        # ── TAB 7 ─────────────────────────────────────────────────
+
+            q3=gr.Textbox(label="Drug Name")
+            btn4=gr.Button("Analyze")
+
+            card=gr.HTML()
+            fig2=gr.Plot()
+
+            btn4.click(fingerprint,q3,[card,fig2])
+
         with gr.Tab("📋 Drug Explorer"):
-            with gr.Row():
-                ex_sys  = gr.Dropdown(["All","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"],
-                    value="All", label="System")
-                ex_dos  = gr.Dropdown(choices=_ALL_DOS, value="All", label="Dosage Form")
-                ex_srch = gr.Textbox(label="Search", placeholder="Brand, compound, manufacturer…")
-                ex_pg   = gr.Number(value=1, label="Page", minimum=1, precision=0)
-            ex_btn  = gr.Button("🔍 Browse Database", variant="primary")
-            ex_info = gr.Markdown("")
-            ex_tbl  = gr.HTML('<div class="ph">Click Browse to explore all 53,584 drug records</div>')
-
-            ex_sys.change(_dos_choices,[ex_sys],[ex_dos])
-            ex_btn.click(tab7_explore,[ex_sys,ex_dos,ex_srch,ex_pg],[ex_tbl,ex_info])
-            ex_srch.submit(tab7_explore,[ex_sys,ex_dos,ex_srch,ex_pg],[ex_tbl,ex_info])
-
-        # ── TAB 8 ─────────────────────────────────────────────────
+
+            sys2=gr.Dropdown(["All","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"],value="All")
+            search=gr.Textbox(label="Search")
+            btn5=gr.Button("Browse")
+
+            table=gr.HTML()
+
+            btn5.click(explorer,[sys2,search],table)
+
         with gr.Tab("ℹ️ About"):
-            gr.Markdown(f"""
-## PharmaBridge — Cross-Medical-System Drug Intelligence
-
-**PharmaBridge** is a master's thesis project — the first NLP-based drug recommendation system
-spanning all 5 major South Asian pharmaceutical traditions simultaneously using the
-Bangladesh National Drug Registry (53,584 records).
-
----
-
-### Dataset Composition
-| Medical System | Records | Share |
-|---|---|---|
-| Allopathic | 36,254 | 67.7% |
-| Unani | 8,460 | 15.8% |
-| Ayurvedic | 5,262 | 9.8% |
-| Homeopathic | 2,580 | 4.8% |
-| Herbal | 1,028 | 1.9% |
-| **Total** | **53,584** | **100%** |
-
-### Technical Architecture
-| Component | Configuration |
-|---|---|
-| Vectorization | TF-IDF, bigrams (1,2), max_features=15,000, sublinear_tf=True |
-| Retrieval | Cosine Similarity on sparse matrix (53,584 × 12,311) |
-| Dim. Reduction | TruncatedSVD, 50 components, 26.2% variance |
-| Clustering | K-Means K=10 (elbow-selected), Silhouette=0.2159 |
-
-### Evaluation Results
-| Metric | Value |
-|---|---|
-| Precision@5 | 97.00% |
-| Precision@10 | 95.50% |
-| Precision@20 | 90.55% |
-| Silhouette Score | 0.2159 |
-
-### App Features
-| Tab | Feature |
-|---|---|
-| 🔍 Smart Search | TF-IDF cosine retrieval with rich card UI + bar chart |
-| ⚖️ Cross-System Compare | Side-by-side 5-system view + radar chart |
-| 📊 Dataset Analytics | Overview dashboard, deep-dive, treemap |
-| 🧬 Drug Fingerprint | Single drug profile + TF-IDF feature bar chart |
-| 🏥 FDA Live Data | OpenFDA labels / adverse events / NDC lookup |
-| 🤖 AI Medical Q&A | Mistral-7B via HuggingFace Inference API |
-| 📋 Drug Explorer | Paginated browse & filter across all 53,584 records |
-
----
-> **Disclaimer:** For research and educational purposes only.
-> Not intended for clinical decision-making.
-> Always consult a qualified healthcare professional for medical advice.
-            """)
-
-if __name__ == "__main__":
-    app.launch()
+
+            gr.Markdown("""
+PharmaBridge is a pharmaceutical AI research system designed to explore drug datasets across multiple medical traditions.
+
+Features:
+
+• Smart Drug Search
+• Cross System Medicine Comparison
+• Dataset Analytics Dashboard
+• Drug Fingerprint Analysis
+• Drug Explorer
+
+Built for research and educational use.
+""")
+
+app.launch()