Update app.py

app.py

@@ -7,64 +7,136 @@ import random
 import os
 from safetensors.torch import load_file
 
-
 # Model və label_encoder yüklənməsi
 def load_model():
-    # Label encoder
-    with open("best_model/label_encoder.pkl", "rb") as f:
-        label_encoder = pickle.load(f)
-
-    # Tokenizer
-    tokenizer = BertTokenizer.from_pretrained("best_model")
-
-    # Model (safetensors avtomatik dəstəklənir)
-    model = BertForSequenceClassification.from_pretrained("best_model", use_safetensors=True)
-    model.eval()
-
-    return tokenizer, model, label_encoder
+    try:
+        # Label encoder
+        with open("best_model/label_encoder.pkl", "rb") as f:
+            label_encoder = pickle.load(f)
+        
+        # Tokenizer
+        tokenizer = BertTokenizer.from_pretrained("best_model")
+        
+        # Model (safetensors avtomatik dəstəklənir)
+        model = BertForSequenceClassification.from_pretrained("best_model", use_safetensors=True)
+        model.eval()
+        
+        print(f"Model uğurla yükləndi. Label sayı: {len(label_encoder.classes_)}")
+        return tokenizer, model, label_encoder
+    
+    except Exception as e:
+        print(f"Model yüklənmə xətası: {e}")
+        # Faylları yoxla
+        if os.path.exists("best_model"):
+            files = os.listdir("best_model")
+            print(f"best_model qovluğundakı fayllar: {files}")
+        else:
+            print("best_model qovluğu mövcud deyil")
+        
+        return None, None, None
 
+# Model yükləmə
 tokenizer, model, label_encoder = load_model()
 
 # Prediction funksiyası
 def predict_disease(text):
+    if tokenizer is None or model is None or label_encoder is None:
+        return "❌ Model yüklənməyib! Xəta var."
+    
     if not text.strip():
-        return "Please enter some symptoms!"
-
+        return "⚠️ Please enter some symptoms!"
+    
     symptoms = [s.strip() for s in text.split(",") if s.strip()]
     if not symptoms:
-        return "Please enter valid symptoms separated by commas!"
-
-    agg_probs = defaultdict(float)
-    n_shuffles = 10
-    for _ in range(n_shuffles):
-        random.shuffle(symptoms)
-        shuffled_text = ", ".join(symptoms)
-        inputs = tokenizer(shuffled_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-        with torch.no_grad():
-            outputs = model(**inputs)
-            probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze()
-        for i, p in enumerate(probs):
-            agg_probs[i] += p.item()
+        return "⚠️ Please enter valid symptoms separated by commas!"
     
-    for k in agg_probs:
-        agg_probs[k] /= n_shuffles
-
-    top_3 = sorted(agg_probs.items(), key=lambda x: x[1], reverse=True)[:3]
-    results = []
-    for idx, prob in top_3:
-        label = label_encoder.classes_[idx]
-        results.append(f"{label} — Probability: {prob*100:.2f}%")
-    return "\n".join(results)
+    try:
+        agg_probs = defaultdict(float)
+        n_shuffles = 10
+        
+        for i in range(n_shuffles):
+            random.shuffle(symptoms)
+            shuffled_text = ", ".join(symptoms)
+            
+            inputs = tokenizer(
+                shuffled_text, 
+                return_tensors="pt", 
+                truncation=True, 
+                padding=True, 
+                max_length=128
+            )
+            
+            with torch.no_grad():
+                outputs = model(**inputs)
+                probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze()
+            
+            for idx, p in enumerate(probs):
+                agg_probs[idx] += p.item()
+        
+        # Ortalama hesabla
+        for k in agg_probs:
+            agg_probs[k] /= n_shuffles
+        
+        # Top 3 nəticə
+        top_3 = sorted(agg_probs.items(), key=lambda x: x[1], reverse=True)[:3]
+        
+        results = ["🏥 Top 3 Predicted Diseases:\n"]
+        for idx, prob in top_3:
+            label = label_encoder.classes_[idx]
+            results.append(f"• **{label}** — Probability: {prob*100:.2f}%")
+        
+        return "\n".join(results)
+    
+    except Exception as e:
+        return f"❌ Prediction xətası: {str(e)}"
 
 # Gradio interface
 iface = gr.Interface(
     fn=predict_disease,
-    inputs=gr.Textbox(lines=2, placeholder="Enter your symptoms separated by commas"),
-    outputs=gr.Textbox(),
-    title="Disease NLP Classifier",
-    description="Enter your symptoms (comma separated) and get top 3 predicted diseases."
+    inputs=gr.Textbox(
+        lines=2, 
+        placeholder="fever, cough, headache, shortness of breath",
+        label="Enter your symptoms (comma separated)"
+    ),
+    outputs=gr.Textbox(label="Predicted Diseases"),
+    title="🏥 Disease NLP Classifier",
+    description="Enter your symptoms separated by commas and get top 3 predicted diseases with confidence scores.",
+    examples=[
+        ["fever, cough, headache"],
+        ["stomach pain, nausea, vomiting"],
+        ["chest pain, shortness of breath"],
+        ["dizziness, fatigue, weakness"],
+        ["skin rash, itching, redness"]
+    ]
 )
 
 # Launch
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
+    if tokenizer and model and label_encoder:
+        print("✅ Model hazırdır, Gradio başladılır...")
+        iface.launch(
+            server_name="0.0.0.0", 
+            server_port=int(os.environ.get("PORT", 7860)),
+            share=True  # Public link yaradır
+        )
+    else:
+        print("❌ Model yüklənmədi, Gradio başladıla bilmir!")
+        print("\nDebug məlumatları:")
+        print(f"Hazırkı qovluq: {os.getcwd()}")
+        print(f"Qovluq məzmunu: {os.listdir('.')}")
+        
+        # Sadə debug interface
+        def debug_info():
+            return f"Debug məlumatları:\nHazırkı qovluq: {os.getcwd()}\nFayllar: {os.listdir('.')}"
+        
+        debug_iface = gr.Interface(
+            fn=debug_info,
+            inputs=gr.Textbox(placeholder="Debug üçün hər hansı mətn yazın"),
+            outputs=gr.Textbox(),
+            title="🔧 Debug Interface"
+        )
+        
+        debug_iface.launch(
+            server_name="0.0.0.0",
+            server_port=int(os.environ.get("PORT", 7860))
+        )