Update app.py

app.py

@@ -144,55 +144,37 @@ Here are some documents that are relevant to the question.
 ```
 """
 # Define the predict function
-def predict(user_input,company):
-
-    filter = "dataset/"+company+"-10-k-2023.pdf"
-    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})
+def predict(user_input, company):
+    filter = f"dataset/{company}-10-k-2023.pdf"
+    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source": filter})
 
-    # Create context_for_query
+    # Create context for query
     context_list = [d.page_content for d in relevant_document_chunks]
-    context_for_query = ".".join(context_list)
+    context_for_query = ".".join(context_list)  # Ensure this is being assigned correctly
 
     # Create messages
     prompt = [
-        {'role':'system', 'content': qna_system_message},
+        {'role': 'system', 'content': qna_system_message},
         {'role': 'user', 'content': qna_user_message_template.format(
-            context=context_for_query,
+            context=context_for_query,  # Passing the correct context
             question=user_input
-            )
-        }
+        )}
     ]
 
-    # Get response from the LLM
     try:
-        response = client.chat.completions.create(
-            model='mistralai/Mixtral-8x7B-Instruct-v0.1',
+        # Get response from the LLM
+        response = openai.ChatCompletion.create(
+            model='gpt-3.5-turbo',
             messages=prompt,
             temperature=0
         )
-
-        prediction = response.choices[0].message.content
+        prediction = response['choices'][0]['message']['content']
 
     except Exception as e:
-        prediction = e
-
-    # While the prediction is made, log both the inputs and outputs to a local log file
-    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
-    # access
-
-    with scheduler.lock:
-        with log_file.open("a") as f:
-            f.write(json.dumps(
-                {
-                    'user_input': user_input,
-                    'retrieved_context': context_for_query,
-                    'model_response': prediction
-                }
-            ))
-            f.write("\n")
+        prediction = f"Error: {str(e)}"
 
     return prediction
-     
+
 
 examples = [
     ["What are the company's policies and frameworks regarding AI ethics, governance, and responsible AI use as detailed in their 10-K reports?", "AWS"],