Update main.py

main.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, File, UploadFile, Depends
+from fastapi import FastAPI, File, UploadFile, Depends, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 
 from typing import List, Dict, Any
@@ -202,30 +202,39 @@ app.state.conversation_chain = None
 @app.post("/upload_files/")
 async def upload_files(files: List[UploadFile] = File(...)):
     file_details = []
-    for file in files:
-        content = await file.read()
-        name = f"{file.filename}"
-        details = {"content": content, "name": name}
-        file_details.append(details)
-
-    app.state.conversational_chain = Conversational_Chain(
-        file_details
-    ).create_conversational_chain()
-    print("conversational_chain_manager created")
+    try:
+        for file in files:
+            content = await file.read()
+            name = f"{file.filename}"
+            details = {"content": content, "name": name}
+            file_details.append(details)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    try:
+        app.state.conversational_chain = Conversational_Chain(
+            file_details
+        ).create_conversational_chain()
+        print("conversational_chain_manager created")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
     return {"message": "ConversationalRetrievalChain is created. Please ask questions."}
 
 
 @app.get("/predict/")
-async def predict(
-    query: str,
-):
-    if app.state.conversation_chain is None:
-        system_prompt = "Answer the question and also ask the user to upload files to ask questions from the files.\n"
-        response = app.state.llm_model.invoke(system_prompt + query)
-        answer = response.content
-    else:
-        response = app.state.conversation_chain.invoke(query)
-        answer = response["answer"]
+async def predict(query: str):
+    try:
+        if app.state.conversation_chain is None:
+            system_prompt = "Answer the question and also ask the user to upload files to ask questions from the files.\n"
+            llm_model = ChatOpenAI()
+            response = llm_model.invoke(system_prompt + query)
+            answer = response.content
+        else:
+            response = app.state.conversation_chain.invoke(query)
+            answer = response["answer"]
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
     print("predict called")
     return {"answer": answer}