Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,7 +7,7 @@ import random
 from collections import defaultdict
 import json
 
-# Label encoder faylını yükləmək
+# Label encoder yükləmə funksiyası
 def load_label_encoder():
     file_path = os.path.join(os.getcwd(), "best_model", "label_encoder.pkl")
     if not os.path.exists(file_path):
@@ -24,88 +24,97 @@ def load_model():
     model_path = os.path.join(os.getcwd(), "best_model")
 
     tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(label_encoder.classes_))
+    model = BertForSequenceClassification.from_pretrained(
+        model_path,
+        num_labels=len(label_encoder.classes_)
+    )
     model.eval()
     return tokenizer, model, label_encoder
 
 # Prediction funksiyası
 def predict_disease(symptoms_text, tokenizer, model, label_encoder):
     symptoms = [s.strip() for s in symptoms_text.split(",") if s.strip()]
-    
     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)
+        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()
-    
+
     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] if idx < len(label_encoder.classes_) else f"Unknown label {idx}"
         results.append({"disease": label, "probability": prob})
-    
+
     return results
 
 # Page config
 st.set_page_config(page_title="Disease API", layout="wide")
 
-# API mode detection
-# API mode detection
+# Query parametrlər
 query_params = st.query_params
-is_api_mode = query_params.get("api", ["false"])[0].lower() == "true"
+is_api_mode = str(query_params.get("api", ["false"])[0]).lower() == "true"
 
-# Load model
+# Model yüklə
 tokenizer, model, label_encoder = load_model()
 
+# API mode
 if is_api_mode:
-    st.markdown("### API Mode")
     symptoms = query_params.get("symptoms", [""])[0]
     if symptoms:
         results = predict_disease(symptoms, tokenizer, model, label_encoder)
-        st.json({
+        api_response = {
             "status": "success",
             "input": symptoms,
             "predictions": results
-        })
+        }
     else:
-        st.json({
+        api_response = {
             "status": "error",
             "message": "symptoms parameter required"
-        })
-
-else:
-    st.title("🏥 Disease Prediction")
-    st.success("Model yükləndi!")
-
-    # Debug: Siniflər
-    st.write("Available classes:", list(label_encoder.classes_))
-
-    # API usage info
-    st.markdown("### API İstifadəsi")
-    space_url = "https://your-username-your-space-name.hf.space"
-    api_example = f"{space_url}/?api=true&symptoms=fever,cough,headache"
-    st.code(api_example)
-
-    text = st.text_area("Simptomları daxil edin (vergüllə ayırın):")
-
-    if st.button("Predict"):
-        if not text.strip():
-            st.warning("Simptomları daxil edin!")
-        else:
-            results = predict_disease(text, tokenizer, model, label_encoder)
-            st.subheader("🔍 Nəticələr:")
-            for result in results:
-                st.write(f"**{result['disease']}** — {result['probability']*100:.2f}%")
+        }
+
+    # JSON olaraq qaytar (raw)
+    st.write(json.dumps(api_response, ensure_ascii=False))
+    st.stop()
+
+# Web interfeys
+st.title("🏥 Disease Prediction")
+st.success("Model yükləndi!")
+
+# Debug: Siniflər
+st.write("Available classes:", list(label_encoder.classes_))
+
+# API usage info
+st.markdown("### API İstifadəsi")
+space_url = "https://your-username-your-space-name.hf.space"
+api_example = f"{space_url}/?api=true&symptoms=fever,cough,headache"
+st.code(api_example)
+
+text = st.text_area("Simptomları daxil edin (vergüllə ayırın):")
+
+if st.button("Predict"):
+    if not text.strip():
+        st.warning("Simptomları daxil edin!")
+    else:
+        results = predict_disease(text, tokenizer, model, label_encoder)
+        st.subheader("🔍 Nəticələr:")
+        for result in results:
+            st.write(f"**{result['disease']}** — {result['probability']*100:.2f}%")