Update app/main.py

app/main.py

@@ -5,7 +5,7 @@ from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from dotenv import load_dotenv
-from kb_embed import search_knowledge_base
+#from kb_embed import search_knowledge_base
 import logging
 
 
@@ -21,14 +21,14 @@ load_dotenv()
 app = FastAPI()
 
 # --- 2. Configure CORS ---
-origins = [
-    "http://localhost:5173",
-    "http://localhost:3000",
-]
+#origins = [
+#    "http://localhost:5173",
+#    "http://localhost:3000",
+#]
 
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=origins,
+    allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
@@ -47,57 +47,40 @@ GEMINI_URL = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.
 async def health_check():
     return {"status": "ok"}
 
+
 @app.post("/chat")
-async def chat_with_gemini(input_data: ChatInput):
+async def chat_with_ai(input_data: ChatInput):
+    """Handle chat interactions using Google Generative AI via requests."""
     try:
-        # Retrieve relevant documents from knowledge base
-
-        kb_results = search_knowledge_base(input_data.user_message, top_k=10)
-        logging.info(f"kb_results are: {kb_results}")
-
-        # Extract relevant context from search results
-        context = ""
-        if kb_results and kb_results.get('documents'):
-            # Limit context to avoid token limits - take top 2 most relevant
-            relevant_docs = kb_results['documents'][0][:2]
-            context = "\n\n".join(relevant_docs)
-          
-        # Construct enhanced prompt with context
-        if context:
-            enhanced_prompt = f"""Use the following knowledge base context to answer the user's question accurately.
-If the context contains relevant information, base your answer on it.
-If the context doesn't help, say you're raising a ticket and provide a ticket number.
-
-Knowledge Base Context:
-{context}
-
-User Question: {input_data.user_message}
-
-Answer:"""
-        else:
-            enhanced_prompt = f"User Question: {input_data.user_message}\n\nAnswer:"
-
-        headers = {"Content-Type": "application/json"}
+        # Gemini expects contents array with role and parts
         payload = {
             "contents": [
                 {
-                    "parts": [{"text": enhanced_prompt}]
+                    "role": "user",
+                    "parts": [{"text": input_data.user_message}],
                 }
-            ]
+            ],
+            #"generationConfig": {
+            #    "temperature": 0.7,
+            #    "maxOutputTokens": 512
+            #}
         }
 
-        response = requests.post(GEMINI_URL, headers=headers, json=payload, verify=False)  # SSL disabled for testing
-        result = response.json()
+        # Make POST request to Gemini API
+        response = requests.post(GEMINI_URL, json=payload,verify=False)
+        response.raise_for_status()
+        data = response.json()
 
-        # Extract Gemini's response
-        bot_response = result["candidates"][0]["content"]["parts"][0]["text"]
+        # Extract text from response
+        bot_response = ""
+        if "candidates" in data and data["candidates"]:
+            parts = data["candidates"][0].get("content", {}).get("parts", [])
+            for part in parts:
+                if "text" in part:
+                    bot_response += part["text"]
 
-        # Include debug info in response
-        debug_info = f"Context found: {'Yes' if context else 'No'}"
-        if context:
-            debug_info += f" (Top {len(relevant_docs)} documents used)"
+        return {"bot_response": bot_response or "No response text."}
 
-        return {"bot_response": bot_response, "debug": debug_info}
 
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))