Update app.py

app.py

@@ -558,36 +558,70 @@ async def chat_endpoint(chat_query: ChatQuery):
 @app.post("/api/resources")
 async def resources_endpoint(profile: MedicalProfile):
     try:
+        # Validate profile input
+        if not profile.chronic_conditions or not profile.daily_symptoms or not profile.food_restrictions or not profile.mental_conditions:
+            raise ValueError("Incomplete profile data provided.")
+
+        # Build context
         context = f"""
         Medical conditions: {', '.join(profile.chronic_conditions)}
         Current symptoms: {', '.join(profile.daily_symptoms)}
         Restrictions: {', '.join(profile.food_restrictions)}
         Mental health: {', '.join(profile.mental_conditions)}
         """
-        query_text = context      
-        query_embedding = embed_query_text(query_text)  # Embed the query text
-        embeddings_data = load_embeddings ()
+        query_text = context
+        
+        # Embed query and fetch embeddings
+        query_embedding = embed_query_text(query_text)
+        if query_embedding is None:
+            raise ValueError("Query embedding generation failed.")
+        
+        embeddings_data = load_embeddings()
+        if not embeddings_data:
+            raise ValueError("Failed to load embeddings data.")
+        
         folder_path = 'downloaded_articles/downloaded_articles'
         initial_results = query_embeddings(query_embedding, embeddings_data, n_results=5)
+        if not initial_results:
+            raise ValueError("No initial results found from query_embeddings.")
+        
+        # Retrieve document texts
         document_ids = [doc_id for doc_id, _ in initial_results]
         document_texts = retrieve_document_texts(document_ids, folder_path)
+        if not document_texts or any(doc is None for doc in document_texts):
+            raise ValueError("Failed to retrieve valid document texts.")
+        
+        # Perform reranking
         cross_encoder = models['cross_encoder']
         scores = cross_encoder.predict([(query_text, doc) for doc in document_texts])
         scored_documents = list(zip(scores, document_ids, document_texts))
-        ranked_docs = scored_documents.sort(key=lambda x: x[0], reverse=True)
+        scored_documents.sort(key=lambda x: x[0], reverse=True)
+
+        # Build resources response
         resources = []
-        for (doc_id, _), score, text in ranked_docs[:10]:
-            doc_info = data['df'][data['df']['id'] == doc_id].iloc[0]
+        for score, doc_id, text in scored_documents[:10]:
+            doc_info = data['df'][data['df']['id'] == doc_id]
+            if doc_info.empty:
+                continue  # Skip missing documents
+            doc_info = doc_info.iloc[0]  # Safe to access the first row now
             resources.append({
                 "id": doc_id,
                 "title": doc_info['title'],
                 "content": text[:200],
                 "score": float(score)
             })
+        
         return {"resources": resources, "success": True}
+
+    except ValueError as ve:
+        # Handle expected errors gracefully
+        raise HTTPException(status_code=400, detail=str(ve))
     except Exception as e:
+        # Handle unexpected errors
+        print(f"Unexpected error: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
+
 @app.post("/api/recipes")
 async def recipes_endpoint(profile: MedicalProfile):
     try: