Update app.py

app.py

@@ -144,9 +144,10 @@ Here are some documents that are relevant to the question.
 ```
 """
 # Define the predict function
-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})
+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})
 
     # Create context_for_query
     context_list = [d.page_content for d in relevant_document_chunks]
@@ -162,20 +163,36 @@ def predict(user_input, company):
         }
     ]
 
-
+    # Get response from the LLM
     try:
-        # Get response from the LLM
-        response = openai.ChatCompletion.create(
-            model='gpt-3.5-turbo',
+        response = client.chat.completions.create(
+            model='mistralai/Mixtral-8x7B-Instruct-v0.1',
             messages=prompt,
             temperature=0
         )
-        prediction = response['choices'][0]['message']['content']
+
+        prediction = response.choices[0].message.content
 
     except Exception as e:
-        prediction = f"Error: {str(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")
 
     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"],