Update app.py

app.py

@@ -22,9 +22,6 @@ MODEL = "meta/llama-4-maverick-17b-128e-instruct"
 AFSCTP_FOLDER = "./frameworks/standards"
 ATQF_FOLDER = "./frameworks/qualifications"
 
-# Knowledge base storage
-KNOWLEDGE_DB_FILE = "./frameworks/knowledge_base.json"
-
 # ---------- AFSCTP SCORECARD (Standards and Competencies) ----------
 AFSCTP_CRITERIA = {
     "S1": {
@@ -177,248 +174,9 @@ ATQF_CRITERIA = {
     }
 }
 
-# ---------- KNOWLEDGE BASE MANAGEMENT ----------
-
-def load_knowledge_base():
-    """Load existing knowledge base or return empty structure"""
-    if os.path.exists(KNOWLEDGE_DB_FILE):
-        try:
-            with open(KNOWLEDGE_DB_FILE, 'r', encoding='utf-8') as f:
-                return json.load(f)
-        except:
-            return {"countries": {}, "last_updated": None}
-    return {"countries": {}, "last_updated": None}
-
-def save_knowledge_base(kb):
-    """Save knowledge base to file"""
-    os.makedirs(os.path.dirname(KNOWLEDGE_DB_FILE), exist_ok=True)
-    kb["last_updated"] = datetime.now().isoformat()
-    with open(KNOWLEDGE_DB_FILE, 'w', encoding='utf-8') as f:
-        json.dump(kb, f, indent=2)
-
-def scan_folders_for_documents():
-    """Scan both folders and return list of all documents with their framework types"""
-    documents = []
-    
-    # Scan AFSCTP folder
-    if os.path.exists(AFSCTP_FOLDER):
-        for f in os.listdir(AFSCTP_FOLDER):
-            if f.lower().endswith('.pdf'):
-                documents.append({
-                    "filename": f,
-                    "framework_type": "Standards and Competencies (AFSCTP)",
-                    "framework_code": "AFSCTP",
-                    "path": os.path.join(AFSCTP_FOLDER, f)
-                })
-    
-    # Scan ATQF folder
-    if os.path.exists(ATQF_FOLDER):
-        for f in os.listdir(ATQF_FOLDER):
-            if f.lower().endswith('.pdf'):
-                documents.append({
-                    "filename": f,
-                    "framework_type": "Qualifications Framework (ATQF/CTQF)",
-                    "framework_code": "ATQF",
-                    "path": os.path.join(ATQF_FOLDER, f)
-                })
-    
-    return documents
-
-def build_knowledge_base_from_documents():
-    """Scan all documents and build/update knowledge base"""
-    kb = load_knowledge_base()
-    documents = scan_folders_for_documents()
-    
-    if not NVIDIA_API_KEY:
-        print("Warning: NVIDIA_API_KEY not set. Cannot build knowledge base.")
-        return kb
-    
-    processed_count = 0
-    for doc in documents:
-        # Check if already processed and up to date
-        doc_id = f"{doc['framework_code']}_{doc['filename']}"
-        if doc_id in kb.get("processed_docs", {}):
-            # Check if file has been modified
-            stored_mtime = kb["processed_docs"][doc_id].get("mtime", 0)
-            current_mtime = os.path.getmtime(doc["path"])
-            if stored_mtime == current_mtime:
-                continue  # Skip already processed files
-        
-        try:
-            print(f"Processing: {doc['filename']}...")
-            text = extract_text_from_file(doc["path"])
-            country = detect_country(text)
-            
-            # Determine criteria dict
-            if doc["framework_code"] == "AFSCTP":
-                criteria_dict = AFSCTP_CRITERIA
-            else:
-                criteria_dict = ATQF_CRITERIA
-            
-            # Evaluate
-            prompt = build_evaluation_prompt(text, criteria_dict, doc["framework_code"])
-            results = call_nvidia_llm(prompt)
-            
-            # Calculate overall score
-            scores = []
-            for cid in criteria_dict.keys():
-                score = results.get(cid, 0)
-                try:
-                    scores.append(int(score))
-                except:
-                    scores.append(0)
-            
-            total_score = sum(scores)
-            max_score = len(criteria_dict) * 5
-            percent = (total_score / max_score) * 100 if max_score > 0 else 0
-            
-            # Store in knowledge base
-            if country not in kb["countries"]:
-                kb["countries"][country] = {}
-            
-            kb["countries"][country][doc["framework_code"]] = {
-                "filename": doc["filename"],
-                "overall_score": round(percent, 1),
-                "criterion_scores": results,
-                "individual_scores": scores,
-                "processed_date": datetime.now().isoformat(),
-                "document_path": doc["path"]
-            }
-            
-            # Track processed document
-            if "processed_docs" not in kb:
-                kb["processed_docs"] = {}
-            kb["processed_docs"][doc_id] = {
-                "mtime": os.path.getmtime(doc["path"]),
-                "processed_date": datetime.now().isoformat()
-            }
-            
-            processed_count += 1
-            
-        except Exception as e:
-            print(f"Error processing {doc['filename']}: {e}")
-    
-    save_knowledge_base(kb)
-    print(f"Knowledge base updated. Processed {processed_count} new/updated documents.")
-    return kb
-
-def get_knowledge_glossary():
-    """Generate glossary display from knowledge base"""
-    kb = load_knowledge_base()
-    
-    if not kb["countries"]:
-        return "### 📚 Knowledge Base\\n\\nNo documents have been processed yet. Upload documents and click 'Refresh Knowledge Base' to build the glossary."
-    
-    md = "### 📚 African Union Teacher Frameworks Knowledge Base\\n\\n"
-    md += f"*Last Updated: {kb.get('last_updated', 'Unknown')[:10] if kb.get('last_updated') else 'Unknown'}*\\n\\n"
-    
-    # Summary statistics
-    total_countries = len(kb["countries"])
-    afsctp_count = sum(1 for c in kb["countries"].values() if "AFSCTP" in c)
-    atqf_count = sum(1 for c in kb["countries"].values() if "ATQF" in c)
-    
-    md += f"**📊 Overview:** {total_countries} countries | {afsctp_count} AFSCTP evaluations | {atqf_count} ATQF/CTQF evaluations\\n\\n"
-    md += "---\\n\\n"
-    
-    # Sort countries alphabetically
-    for country in sorted(kb["countries"].keys()):
-        data = kb["countries"][country]
-        md += f"#### 🌍 {country}\\n\\n"
-        
-        for framework_code, eval_data in data.items():
-            framework_name = "AFSCTP (Standards)" if framework_code == "AFSCTP" else "ATQF/CTQF (Qualifications)"
-            score = eval_data["overall_score"]
-            
-            # Color code the score
-            if score >= 75:
-                emoji = "🟢"
-                color = "green"
-            elif score >= 50:
-                emoji = "🟡"
-                color = "orange"
-            else:
-                emoji = "🔴"
-                color = "red"
-            
-            md += f"**{framework_name}:** {emoji} **{score:.1f}%** alignment\\n"
-            
-            # Show individual criterion scores
-            scores = eval_data.get("individual_scores", [])
-            criteria_dict = AFSCTP_CRITERIA if framework_code == "AFSCTP" else ATQF_CRITERIA
-            
-            if scores and len(scores) == len(criteria_dict):
-                score_details = []
-                for (cid, data_criteria), score_val in zip(criteria_dict.items(), scores):
-                    score_emoji = "🟢" if score_val >= 4 else "🟡" if score_val == 3 else "🔴"
-                    score_details.append(f"{cid}:{score_emoji}{score_val}")
-                md += f"  *Scores:* {' | '.join(score_details)}\\n"
-            
-            md += "\\n"
-        
-        md += "---\\n\\n"
-    
-    return md
-
-def get_country_comparison_data():
-    """Get data for country comparison charts"""
-    kb = load_knowledge_base()
-    
-    countries = []
-    afsctp_scores = []
-    atqf_scores = []
-    
-    for country in sorted(kb["countries"].keys()):
-        countries.append(country)
-        data = kb["countries"][country]
-        afsctp_scores.append(data.get("AFSCTP", {}).get("overall_score", 0))
-        atqf_scores.append(data.get("ATQF", {}).get("overall_score", 0))
-    
-    return countries, afsctp_scores, atqf_scores
-
-def generate_comparison_charts():
-    """Generate comparison charts for knowledge base"""
-    countries, afsctp_scores, atqf_scores = get_country_comparison_data()
-    
-    if not countries:
-        return None, None
-    
-    # Bar chart comparing countries
-    fig, ax = plt.subplots(figsize=(14, max(6, len(countries) * 0.5)))
-    
-    y_pos = np.arange(len(countries))
-    height = 0.35
-    
-    # Only show frameworks that have data
-    has_afsctp = any(s > 0 for s in afsctp_scores)
-    has_atqf = any(s > 0 for s in atqf_scores)
-    
-    if has_afsctp:
-        bars1 = ax.barh(y_pos - height/2 if has_atqf else y_pos, afsctp_scores, height, 
-                        label='AFSCTP (Standards)', color='#1565c0', alpha=0.8)
-    if has_atqf:
-        bars2 = ax.barh(y_pos + height/2 if has_afsctp else y_pos, atqf_scores, height,
-                        label='ATQF/CTQF (Qualifications)', color='#2e7d32', alpha=0.8)
-    
-    ax.set_yticks(y_pos)
-    ax.set_yticklabels(countries)
-    ax.set_xlabel('Alignment Score (%)', fontsize=12, fontweight='bold')
-    ax.set_title('Country Comparison: Framework Alignment Scores', fontsize=14, fontweight='bold')
-    ax.set_xlim(0, 100)
-    ax.axvline(x=50, color='gray', linestyle='--', alpha=0.5, label='50% threshold')
-    ax.legend(loc='lower right')
-    
-    plt.tight_layout()
-    buf = BytesIO()
-    plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
-    buf.seek(0)
-    chart_img = base64.b64encode(buf.read()).decode()
-    plt.close()
-    
-    return chart_img
-
 def get_criteria_display(criteria_dict, framework_name):
     """Generate markdown display of criteria for a specific framework"""
-    md = f"### 📋 {framework_name} Evaluation Criteria\\n\\n"
+    md = f"### 📋 {framework_name} Evaluation Criteria\n\n"
     
     sections = {}
     for cid, data in criteria_dict.items():
@@ -428,14 +186,14 @@ def get_criteria_display(criteria_dict, framework_name):
         sections[section].append((cid, data))
     
     for section, items in sections.items():
-        md += f"\\n#### {section}\\n\\n"
+        md += f"\n#### {section}\n\n"
         for cid, data in items:
-            md += f"**{cid} - {data['short_label']}**\\n"
-            md += "<details><summary>View Scoring Descriptors (0-5)</summary>\\n\\n"
+            md += f"**{cid} - {data['short_label']}**\n"
+            md += "<details><summary>View Scoring Descriptors (0-5)</summary>\n\n"
             for level, desc in data['levels'].items():
                 emoji = "🔴" if level <= 1 else "🟡" if level <= 3 else "🟢"
-                md += f"{emoji} **{level}:** {desc}\\n\\n"
-            md += "</details>\\n\\n"
+                md += f"{emoji} **{level}:** {desc}\n\n"
+            md += "</details>\n\n"
     
     return md
 
@@ -467,33 +225,15 @@ def get_folder_for_framework(framework_type):
     else:
         return ATQF_FOLDER
 
-def get_available_framework_categories():
-    """Get list of available framework categories for upload selection"""
-    categories = []
-    
-    # Check AFSCTP
-    if os.path.exists(AFSCTP_FOLDER) and any(f.endswith('.pdf') for f in os.listdir(AFSCTP_FOLDER)):
-        categories.append("Standards and Competencies (AFSCTP)")
-    else:
-        categories.append("Standards and Competencies (AFSCTP) [No documents yet]")
-    
-    # Check ATQF
-    if os.path.exists(ATQF_FOLDER) and any(f.endswith('.pdf') for f in os.listdir(ATQF_FOLDER)):
-        categories.append("Qualifications Framework (ATQF/CTQF)")
-    else:
-        categories.append("Qualifications Framework (ATQF/CTQF) [No documents yet]")
-    
-    return categories
-
 def extract_text_from_file(file_path):
     if file_path.endswith('.pdf'):
         with open(file_path, 'rb') as f:
             reader = PyPDF2.PdfReader(f)
             text = "".join(page.extract_text() for page in reader.pages)
-            return re.sub(r'\\s+', ' ', text)
+            return re.sub(r'\s+', ' ', text)
     elif file_path.endswith('.docx'):
         doc = Document(file_path)
-        return "\\n".join([para.text for para in doc.paragraphs])
+        return "\n".join([para.text for para in doc.paragraphs])
     else:
         with open(file_path, 'r', encoding='utf-8') as f:
             return f.read()
@@ -523,7 +263,7 @@ def detect_country(document_text):
         response = requests.post(NVIDIA_URL, headers=headers, json=payload)
         if response.status_code == 200:
             country = response.json()["choices"][0]["message"]["content"].strip()
-            country = country.replace("The ", "").replace("the ", "").replace(".", "").replace("\\n", " ").strip()
+            country = country.replace("The ", "").replace("the ", "").replace(".", "").replace("\n", " ").strip()
             return country if country else "Unknown Country"
     except:
         pass
@@ -534,17 +274,17 @@ def build_evaluation_prompt(document_text, criteria_dict, framework_name):
     prompt_parts = []
     prompt_parts.append(f"You are evaluating a national teacher framework against the {framework_name}. ")
     prompt_parts.append("For each criterion below, select the SINGLE best description (0-5) that matches the document content. ")
-    prompt_parts.append("Return ONLY a JSON object where keys are criterion IDs (e.g., 'S1' or 'Q1') and values are integers 0-5. No explanations.\\n")
+    prompt_parts.append("Return ONLY a JSON object where keys are criterion IDs (e.g., 'S1' or 'Q1') and values are integers 0-5. No explanations.\n")
     
     for cid, data in criteria_dict.items():
-        prompt_parts.append(f"\\n{cid} - {data['short_label']}:")
+        prompt_parts.append(f"\n{cid} - {data['short_label']}:")
         for level, desc in data['levels'].items():
             prompt_parts.append(f"  [{level}] {desc}")
     
-    prompt_parts.append(f"\\n\\nDOCUMENT TEXT:\\n{document_text[:20000]}\\n")
-    prompt_parts.append("\\nRespond with valid JSON only: {\\\"S1\\\": 4, \\\"S2\\\": 3, ...} or {\\\"Q1\\\": 5, \\\"Q2\\\": 4, ...}")
+    prompt_parts.append(f"\n\nDOCUMENT TEXT:\n{document_text[:20000]}\n")
+    prompt_parts.append("\nRespond with valid JSON only: {\"S1\": 4, \"S2\": 3, ...} or {\"Q1\": 5, \"Q2\": 4, ...}")
     
-    return "\\n".join(prompt_parts)
+    return "\n".join(prompt_parts)
 
 def call_nvidia_llm(prompt):
     headers = {"Authorization": f"Bearer {NVIDIA_API_KEY}", "Content-Type": "application/json"}
@@ -569,7 +309,7 @@ def call_nvidia_llm(prompt):
     try:
         return json.loads(content)
     except json.JSONDecodeError:
-        match = re.search(r'\\{.*\\}', content, re.DOTALL)
+        match = re.search(r'\{.*\}', content, re.DOTALL)
         if match:
             return json.loads(match.group())
         raise Exception(f"Could not parse JSON from: {content[:200]}")
@@ -586,7 +326,7 @@ def generate_charts(df, country, framework_name):
     bars = plt.barh(labels, values, color=colors, edgecolor='black', linewidth=0.5)
     plt.xlim(0, 5)
     plt.xlabel("Score (0-5)", fontsize=12, fontweight='bold')
-    plt.title(f"{country}\\n{framework_name} Alignment Evaluation", fontsize=16, fontweight='bold', pad=20)
+    plt.title(f"{country}\n{framework_name} Alignment Evaluation", fontsize=16, fontweight='bold', pad=20)
     plt.axvline(x=3, color='gray', linestyle='--', alpha=0.5)
     
     for i, (bar, score) in enumerate(zip(bars, values)):
@@ -613,7 +353,7 @@ def generate_charts(df, country, framework_name):
     ax.set_ylim(0, 5)
     ax.set_yticks([1, 2, 3, 4, 5])
     ax.set_yticklabels(['1', '2', '3', '4', '5'], color="grey", size=8)
-    ax.set_title(f"{country}\\nAlignment Profile", size=16, fontweight='bold', pad=20)
+    ax.set_title(f"{country}\nAlignment Profile", size=16, fontweight='bold', pad=20)
     radar_buf = BytesIO()
     plt.savefig(radar_buf, format='png', dpi=150, bbox_inches='tight')
     radar_buf.seek(0)
@@ -645,7 +385,7 @@ def generate_charts(df, country, framework_name):
     ax.set_xticklabels(['0%', '25%', '50%', '75%', '100%'], fontsize=11)
     ax.set_yticks([])
     ax.set_xlabel(f"Overall Alignment Score", fontsize=14, fontweight='bold')
-    ax.set_title(f"{country}\\n{percent:.1f}% Aligned with {framework_name}", fontsize=18, fontweight='bold', pad=15)
+    ax.set_title(f"{country}\n{percent:.1f}% Aligned with {framework_name}", fontsize=18, fontweight='bold', pad=15)
     ax.text(50, 0, f"{percent:.1f}%", ha='center', va='center', fontsize=20, fontweight='bold', color='white' if percent > 50 else 'black')
     
     gauge_buf = BytesIO()
@@ -677,17 +417,17 @@ def generate_pdf_report(df, country, framework_name, percent, bar_img_data, rada
         weak = df[df['Score'] <= 2]['Label'].tolist()
         moderate = df[df['Score'] == 3]['Label'].tolist()
         
-        summary_text.append(f"\\nStrengths (Score 4-5): {len(strong)} criteria")
+        summary_text.append(f"\nStrengths (Score 4-5): {len(strong)} criteria")
         if strong:
             for s in strong:
                 summary_text.append(f"  ✓ {s}")
         
-        summary_text.append(f"\\nModerate Alignment (Score 3): {len(moderate)} criteria")
+        summary_text.append(f"\nModerate Alignment (Score 3): {len(moderate)} criteria")
         if moderate:
             for m in moderate:
                 summary_text.append(f"  ~ {m}")
                 
-        summary_text.append(f"\\nPriority Areas (Score 0-2): {len(weak)} criteria")
+        summary_text.append(f"\nPriority Areas (Score 0-2): {len(weak)} criteria")
         if weak:
             for w in weak:
                 summary_text.append(f"  ✗ {w}")
@@ -872,136 +612,66 @@ ATQF_DISPLAY = get_criteria_display(ATQF_CRITERIA, "ATQF/CTQF (Qualifications)")
 # Initialize folders and get initial choices
 os.makedirs(AFSCTP_FOLDER, exist_ok=True)
 os.makedirs(ATQF_FOLDER, exist_ok=True)
-
-# Build knowledge base on startup (if API key available)
-if NVIDIA_API_KEY:
-    print("Building knowledge base from existing documents...")
-    build_knowledge_base_from_documents()
-
 INITIAL_DROPDOWN_CHOICES = get_frameworks_for_type("Standards and Competencies (AFSCTP)")
-INITIAL_GLOSSARY = get_knowledge_glossary()
-INITIAL_COMPARISON_CHART = generate_comparison_charts()
 
 # Gradio UI
-with gr.Blocks(title="African Union Teacher Frameworks Knowledge & Assessment Platform") as demo:
-    gr.Markdown("# 🌍 African Union Teacher Frameworks Knowledge & Assessment Platform")
-    gr.Markdown("Explore existing evaluations in the **Knowledge Base** or evaluate new documents against the **African Framework of Standards and Competences for Teachers (AFSCTP)** or the **Continental Teacher Qualification Framework (ATQF/CTQF)**.")
-    
-    # Create tabs for Knowledge Base and Evaluation
-    with gr.Tabs() as tabs:
-        
-        # ========== KNOWLEDGE BASE TAB ==========
-        with gr.TabItem("📚 Knowledge Base", id="knowledge"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### Knowledge Base Controls")
-                    refresh_kb_btn = gr.Button("🔄 Refresh Knowledge Base", variant="primary")
-                    kb_status = gr.Textbox(label="Status", value="Ready", interactive=False)
-                    
-                    gr.Markdown("---")
-                    gr.Markdown("### 📊 Statistics")
-                    kb_stats = gr.Markdown("Click 'Refresh' to see statistics")
-                    
-                with gr.Column(scale=3):
-                    gr.Markdown("### Country Framework Scores Glossary")
-                    glossary_output = gr.Markdown(INITIAL_GLOSSARY)
+with gr.Blocks(title="African Union Teacher Frameworks Self Assessment Tool") as demo:
+    gr.Markdown("# African Union Teacher Frameworks Self Assessment Tool")
+    gr.Markdown("Evaluate national teacher frameworks against the **African Framework of Standards and Competences for Teachers (AFSCTP)** or the **Continental Teacher Qualification Framework (ATQF/CTQF)**.")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Dynamic criteria display based on selection
+            with gr.Accordion("View Evaluation Criteria & Scoring Descriptors", open=False):
+                criteria_markdown = gr.Markdown(AFSCTP_DISPLAY)
             
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("### Country Comparison")
-                    if INITIAL_COMPARISON_CHART:
-                        comparison_html = gr.HTML(f'<img src="data:image/png;base64,{INITIAL_COMPARISON_CHART}" style="width:100%; max-width:1200px;">')
-                    else:
-                        comparison_html = gr.HTML("No data available for comparison. Add documents and refresh.")
-        
-        # ========== EVALUATION TAB ==========
-        with gr.TabItem("🔍 Evaluate Document", id="evaluate"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    # Dynamic criteria display based on selection
-                    with gr.Accordion("View Evaluation Criteria & Scoring Descriptors", open=False):
-                        criteria_markdown = gr.Markdown(AFSCTP_DISPLAY)
-                    
-                    gr.Markdown("### Framework Selection")
-                    
-                    # Framework type selector (default to Standards)
-                    framework_selector = gr.Dropdown(
-                        label="Select Evaluation Framework",
-                        choices=["Standards and Competencies (AFSCTP)", "Qualifications Framework (ATQF/CTQF)"],
-                        value="Standards and Competencies (AFSCTP)",
-                        interactive=True
-                    )
-                    
-                    gr.Markdown("### Input Selection")
-                    
-                    # Dropdown for pre-loaded frameworks - now dynamic based on type
-                    framework_dropdown = gr.Dropdown(
-                        label="Select Pre-loaded Framework",
-                        choices=INITIAL_DROPDOWN_CHOICES,
-                        value=INITIAL_DROPDOWN_CHOICES[0] if INITIAL_DROPDOWN_CHOICES else None,
-                        interactive=True
-                    )
-                    
-                    gr.Markdown("**OR Upload New Document**")
-                    
-                    # File upload with framework category selection
-                    with gr.Group():
-                        file_input = gr.File(label="Upload Document", file_types=[".pdf", ".docx", ".txt"])
-                        upload_framework_selector = gr.Dropdown(
-                            label="Select Framework Category for Upload",
-                            choices=get_available_framework_categories(),
-                            value=get_available_framework_categories()[0] if get_available_framework_categories() else None,
-                            interactive=True
-                        )
-                    
-                    evaluate_btn = gr.Button("Evaluate Framework", variant="primary")
-                    status_text = gr.Textbox(label="Status", value="Waiting", interactive=False)
-                    country_text = gr.Textbox(label="Detected Country", value="Unknown", interactive=False)
-                    
-                    pdf_output = gr.File(label="Download PDF Report", visible=False)
-                    
-                with gr.Column(scale=2):
-                    summary_output = gr.Markdown("")
-                    gauge_output = gr.HTML("")
-
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("### Detailed Scores by Criterion")
-                    bar_output = gr.HTML()
-                with gr.Column():
-                    gr.Markdown("### Alignment Profile")
-                    radar_output = gr.HTML()
-
-            with gr.Row():
-                table_output = gr.Dataframe(label="Detailed Evaluation Results")
-    
-    # ========== EVENT HANDLERS ==========
-    
-    # Refresh knowledge base
-    def refresh_knowledge_base():
-        if not NVIDIA_API_KEY:
-            return "❌ NVIDIA_API_KEY not set. Cannot refresh.", "No data available", "Please configure NVIDIA_API_KEY"
-        try:
-            kb = build_knowledge_base_from_documents()
-            glossary = get_knowledge_glossary()
-            chart = generate_comparison_charts()
+            gr.Markdown("### Framework Selection")
             
-            # Update statistics
-            total_countries = len(kb.get("countries", {}))
-            total_docs = len(kb.get("processed_docs", {}))
-            stats_text = f"**{total_countries}** countries evaluated\\n**{total_docs}** documents processed"
+            # Framework type selector (default to Standards)
+            framework_selector = gr.Dropdown(
+                label="Select Evaluation Framework",
+                choices=["Standards and Competencies (AFSCTP)", "Qualifications Framework (ATQF/CTQF)"],
+                value="Standards and Competencies (AFSCTP)",
+                interactive=True
+            )
             
-            chart_html = f'<img src="data:image/png;base64,{chart}" style="width:100%; max-width:1200px;">' if chart else "No comparison data available"
+            gr.Markdown("### Input Selection")
             
-            return "✅ Knowledge base refreshed", stats_text, glossary, chart_html
-        except Exception as e:
-            return f"❌ Error: {str(e)}", "Error occurred", f"Error: {str(e)}"
-    
-    refresh_kb_btn.click(
-        fn=refresh_knowledge_base,
-        outputs=[kb_status, kb_stats, glossary_output, comparison_html]
-    )
-    
+            # Dropdown for pre-loaded frameworks - now dynamic based on type
+            framework_dropdown = gr.Dropdown(
+                label="Select Pre-loaded Framework",
+                choices=INITIAL_DROPDOWN_CHOICES,
+                value=INITIAL_DROPDOWN_CHOICES[0] if INITIAL_DROPDOWN_CHOICES else None,
+                interactive=True
+            )
+            
+            gr.Markdown("**OR**")
+            
+            # File upload
+            file_input = gr.File(label="Upload Custom Document", file_types=[".pdf", ".docx", ".txt"])
+            
+            evaluate_btn = gr.Button("Evaluate Framework", variant="primary")
+            status_text = gr.Textbox(label="Status", value="Waiting", interactive=False)
+            country_text = gr.Textbox(label="Detected Country", value="Unknown", interactive=False)
+            
+            
+            pdf_output = gr.File(label="Download PDF Report", visible=False)
+            
+        with gr.Column(scale=2):
+            summary_output = gr.Markdown("")
+            gauge_output = gr.HTML("")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Detailed Scores by Criterion")
+            bar_output = gr.HTML()
+        with gr.Column():
+            gr.Markdown("### Alignment Profile")
+            radar_output = gr.HTML()
+
+    with gr.Row():
+        table_output = gr.Dataframe(label="Detailed Evaluation Results")
+
     # Update dropdown and criteria when framework type changes
     def on_framework_change(framework_type):
         choices = get_frameworks_for_type(framework_type)
@@ -1018,20 +688,9 @@ with gr.Blocks(title="African Union Teacher Frameworks Knowledge & Assessment Pl
     )
     
     # Main evaluation handler
-    def handle_evaluation(file, framework_type, dropdown_selection, upload_framework):
-        """Handle evaluation with proper framework type selection"""
-        # If file is uploaded, use the upload framework selector
-        if file is not None:
-            # Clean up the framework type (remove [No documents yet] suffix if present)
-            clean_framework = upload_framework.split(" [")[0] if upload_framework else framework_type
-            return process_inputs(file, clean_framework, None)
-        else:
-            # Use the main framework selector for dropdown selection
-            return process_inputs(None, framework_type, dropdown_selection)
-    
     evaluate_btn.click(
-        fn=handle_evaluation,
-        inputs=[file_input, framework_selector, framework_dropdown, upload_framework_selector],
+        fn=process_inputs,
+        inputs=[file_input, framework_selector, framework_dropdown],
         outputs=[table_output, summary_output, bar_output, radar_output, gauge_output, 
                 gr.Number(visible=False), status_text, country_text, pdf_output]
     ).then(