Update app.py

app.py

@@ -18,7 +18,7 @@ import re
 
 
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-vector_store= FAISS.load_local("vector_db/", embeddings)
+vector_store= FAISS.load_local("vector_db/", embeddings, , allow_dangerous_deserialization=True)
 
 repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1"
 llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.01, "max_new_tokens": 4096})
@@ -29,9 +29,7 @@ retriever = vector_store.as_retriever(
 )
 
 df=pd.read_csv('data/Gretel_Data.csv')
-averages = df.mean().to_dict()
-
-# agent = Agent([df], config={"llm": llm, 'verbose':True})
+averages = df.mean(numeric_only=True).to_dict()
 
 global unique_columns
 unique_columns = [
@@ -132,11 +130,11 @@ network_features  = {
     ]
 }
 
-def echo(message, history):
+def echo(query, history):
   try:
     qa=RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
-    message= " <s> [INST] You are a senior telecom network engineer having access to troubleshooting tickets data and other technical and product documentation. Stick to the knowledge provided. Search through the product documentation pdfs first before scanning the tickets to generate the answer. Return only the helpful answer. Question:" + message + '[/INST]'
-    result= qa({"query":message})
+    query= " <s> [INST] You are a senior telecom network engineer having access to troubleshooting tickets data and other technical and product documentation. Stick to the knowledge provided. Search through the product documentation pdfs first before scanning the tickets to generate the answer. Return only the helpful answer. Question:" + query + '[/INST]'
+    result= qa({"query":query})
     answer= result['result'].split('Helpful Answer:')[-1]
     for word in target_words:
       if re.search(r'\b' + re.escape(word) + r'\b', answer, flags=re.IGNORECASE):
@@ -176,17 +174,6 @@ def echo(message, history):
     error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)
     return error_message, error_message
 
-# def echo_agent(message, history):
-#   message="There are 2 df's. If you find a KeyError check for the same in the other df." + "<br>" + message
-#   try:
-#     with io.StringIO() as buffer:
-#       with contextlib.redirect_stdout(buffer):
-#         result= agent.chat(message)
-#     return result
-#   except Exception as e:
-#     error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)
-#     return error_message
-
 demo_agent = gr.Blocks(
         title="Network Ticket Knowledge Management",
         theme=gr.themes.Soft(),
@@ -229,14 +216,5 @@ with demo_agent:
                 inputs=[message]
                 )
 
-  # with gr.Tab('Sam'):
-  #   with gr.Row():
-  #     message_agent = gr.Text(label="Input Query")
-  #   with gr.Row():
-  #     reply_agent = gr.Text(label="Answer")
-
-  #   btn2 = gr.Button("Submit")
-  #   btn2.click(echo_agent, inputs=[message_agent], outputs=[reply_agent])
-
 
 demo_agent.launch(share=True,debug=True,auth=("admin", "Sam&Clara"))