Upload app.py with huggingface_hub

app.py

@@ -10,18 +10,18 @@ from datetime import datetime, timezone
 from huggingface_hub import HfApi
 import plotly.graph_objects as go
 
-# --- KONFIGŪRACIJA (DEVELOPER PLAN) ---
+# --- KONFIGŪRACIJA ---
 MODEL_NAME = "ProsusAI/finbert"
 TARGET_DATASET = "Vycka12/Base"
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
-# Jūsų specifinis Developer Endpoint
+# API Raktas ir URL (Griežtai nustatyti)
 CRYPTOPANIC_API_KEY = "6c0f988f9e33170ccd183c6a14b34e8c2ad0867f"
 CRYPTOPANIC_URL = "https://cryptopanic.com/api/developer/v2/posts/"
 
 # Globalūs kintamieji
 news_buffer = []
-stats = {"bullish": 0, "bearish": 0, "neutral": 0, "overall": "Laukiama...", "status": "Inicijuojama..."}
+stats = {"bullish": 0, "bearish": 0, "neutral": 0, "overall": "Inicijuojama...", "status": "Startuoja..."}
 
 # Krauname AI
 print("⌛ Kraunamas AI modelis...")
@@ -38,29 +38,33 @@ class SentimentSystem:
 
     def fetch_and_analyze(self):
         global news_buffer, stats
-        stats["status"] = "Siurbiamos naujienos (24h vėlavimas)..."
+        stats["status"] = "Gaunamos naujienos..."
         
+        # Pataisyti parametrai - tik būtiniausi
         params = {
             "auth_token": CRYPTOPANIC_API_KEY,
             "kind": "news"
         }
 
         try:
-            resp = requests.get(CRYPTOPANIC_URL, params=params, timeout=15)
+            resp = requests.get(CRYPTOPANIC_URL, params=params, timeout=20)
             
             if resp.status_code == 200:
-                raw_news = resp.json().get("results", [])
+                data = resp.json()
+                raw_news = data.get("results", [])
+                
                 if not raw_news:
-                    stats["status"] = "⚠️ Naujienų kol kas nėra (limitai?)."
+                    stats["status"] = "⚠️ API grąžino tuščią sąrašą."
                     return
                 
                 temp_news = []
                 pos, neg, neut = 0, 0, 0
                 
-                for item in raw_news[:15]:
+                for item in raw_news:
                     title = item.get("title", "")
                     if not title: continue
                     
+                    # AI Analizė
                     if sentiment_pipeline:
                         result = sentiment_pipeline(title[:512])[0]
                         label = result['label']
@@ -68,6 +72,7 @@ class SentimentSystem:
                     else:
                         label, score = 'neutral', 0.5
 
+                    # Emocijos
                     if label == 'positive': 
                         status, emo = "🟢 BULLISH", "🚀"
                         pos += 1
@@ -78,32 +83,42 @@ class SentimentSystem:
                         status, emo = "⚪ NEUTRAL", "➖"
                         neut += 1
 
-                    temp_news.append([emo, status, title, f"{round(score*100)}%"])
+                    # Laikas
+                    pub_time = item.get("published_at", "")[:16].replace("T", " ")
+                    
+                    temp_news.append([emo, status, title, f"{round(score*100)}%", pub_time])
 
                 stats["bullish"] = pos
                 stats["bearish"] = neg
                 stats["neutral"] = neut
-                if pos > neg: stats["overall"] = "OPTIMIZMAS 📈"
-                elif neg > pos: stats["overall"] = "BAIMĖ 📉"
-                else: stats["overall"] = "LAUKIMAS ⚖️"
+                
+                total = pos + neg + neut
+                if total > 0:
+                    ratio = (pos - neg) / total
+                    if ratio > 0.2: stats["overall"] = "OPTIMIZMAS 📈"
+                    elif ratio < -0.2: stats["overall"] = "BAIMĖ 📉"
+                    else: stats["overall"] = "NEUTRALU ⚖️"
                 
                 news_buffer = temp_news
-                stats["status"] = f"Analizė baigta {datetime.now().strftime('%H:%M:%S')}"
+                stats["status"] = f"Atnaujinta: {datetime.now().strftime('%H:%M:%S')}"
             else:
                 stats["status"] = f"❌ API Klaida: {resp.status_code}"
         except Exception as e:
-            stats["status"] = f"❌ Sistemos klaida: {str(e)}"
+            stats["status"] = f"❌ Klaida: {str(e)}"
 
     def create_gauge(self):
-        total = stats["bullish"] + stats["bearish"]
+        total = stats["bullish"] + stats["bearish"] + stats["neutral"]
         val = 50
         if total > 0:
-            val = ((stats["bullish"] - stats["bearish"]) / total) * 50 + 50
+            # Formulė: 50 + (teigiami - neigiami) * koeficientas
+            # Jei visi teigiami -> 100, visi neigiami -> 0
+            net_sentiment = (stats["bullish"] - stats["bearish"]) / total
+            val = 50 + (net_sentiment * 50)
         
         fig = go.Figure(go.Indicator(
             mode = "gauge+number",
             value = val,
-            title = {'text': f"Nuotaika (vakar): {stats['overall']}"},
+            title = {'text': f"Rinkos Emocija: {stats['overall']}"},
             gauge = {
                 'axis': {'range': [0, 100]},
                 'bar': {'color': "black"},
@@ -114,53 +129,59 @@ class SentimentSystem:
                 ],
             }
         ))
-        fig.update_layout(height=280, margin=dict(l=20, r=20, t=40, b=20), paper_bgcolor="rgba(0,0,0,0)")
+        fig.update_layout(height=300, margin=dict(l=20, r=20, t=50, b=20))
         return fig
 
 sys_analyzer = SentimentSystem()
 
 def update_loop():
+    # Pirmas paleidimas po 10 sek
+    time.sleep(10)
+    sys_analyzer.fetch_and_analyze()
+    
     while True:
+        # Atnaujiname kas 4 valandas (taupome limitą)
+        time.sleep(14400)
         sys_analyzer.fetch_and_analyze()
-        # Laukiame 8 valandas (taupome 100 req/mėn limitą)
-        time.sleep(28800)
 
 def get_ui_data():
     gauge = sys_analyzer.create_gauge()
-    df = pd.DataFrame(news_buffer, columns=["Emoji", "Verdiktas", "Antraštė", "Pasitikėjimas"])
-    if df.empty:
-        df = pd.DataFrame([["-", "-", "Kraunama...", "-"]], columns=["Emoji", "Verdiktas", "Antraštė", "Pasitikėjimas"])
+    cols = ["Emoji", "Verdiktas", "Antraštė", "Pasitikėjimas", "Laikas"]
+    
+    if not news_buffer:
+        df = pd.DataFrame([["-", "-", "Kraunama...", "-", "-"]], columns=cols)
+    else:
+        df = pd.DataFrame(news_buffer, columns=cols)
     
-    status_text = f"### 📊 Suvestinė (Developer Plan)\n**Būsena:** {stats['status']}\n**SVARBU:** Naujienos vėluoja 24h dėl plano ribojimų."
+    status_text = f"### 📊 Statistika\n**Būsena:** {stats['status']}\n**Geros:** {stats['bullish']} | **Blogos:** {stats['bearish']} | **Viso:** {stats['bullish']+stats['bearish']+stats['neutral']}"
     return gauge, df, status_text
 
-# --- GRADIO ---
-with gr.Blocks(title="Sentiment AI Analyzer", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🧠 Crypto Sentimentų Vertėjas (AI)")
-    gr.Markdown("Pastaba: Naudojamas Developer planas (24 valandų naujienų vėlavimas).")
+# --- GRADIO UI ---
+with gr.Blocks(title="Sentiment AI", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 Sentiment AI Analyzer")
+    gr.Markdown("Analizuoja realias crypto naujienas naudodamas ProsusAI FinBERT modelį.")
     
     with gr.Row():
-        with gr.Column(scale=2):
+        with gr.Column():
             gauge_output = gr.Plot(label="Nuotaika")
-        with gr.Column(scale=1):
-            info_output = gr.Markdown("Inicijuojama sistema...")
-            refresh_btn = gr.Button("🔄 Tikrinti naujienas (Atsargiai - limitai!)", variant="primary")
-
-    gr.Markdown("### 📰 Paskutinės perskaitytos naujienos:")
-    table_output = gr.Dataframe(
-        headers=["Emoji", "Verdiktas", "Antraštė", "Pasitikėjimas"],
-        datatype=["str", "str", "str", "str"],
-        interactive=False
-    )
+        with gr.Column():
+            status_output = gr.Markdown("Laukiama duomenų...")
+            refresh_btn = gr.Button("🔄 Atnaujinti Dabar", variant="primary")
 
-    refresh_btn.click(fn=sys_analyzer.fetch_and_analyze)
-    refresh_btn.click(fn=get_ui_data, outputs=[gauge_output, table_output, info_output])
-    
-    demo.load(sys_analyzer.fetch_and_analyze)
-    demo.load(get_ui_data, outputs=[gauge_output, table_output, info_output])
+    gr.Markdown("### 📰 Naujienų srautas ir AI vertinimas")
+    table_output = gr.Dataframe(interactive=False)
+
+    # Eventai
+    refresh_btn.click(fn=sys_analyzer.fetch_and_analyze).then(
+        fn=get_ui_data, outputs=[gauge_output, table_output, status_output]
+    )
     
+    # Auto-start
     threading.Thread(target=update_loop, daemon=True).start()
-    gr.Timer(60).tick(get_ui_data, outputs=[gauge_output, table_output, info_output])
+    
+    # UI atnaujinimas (tik vaizdo, ne duomenų siurbimo)
+    demo.load(get_ui_data, outputs=[gauge_output, table_output, status_output])
+    gr.Timer(5).tick(get_ui_data, outputs=[gauge_output, table_output, status_output])
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)