Update app.py

app.py

@@ -40,14 +40,12 @@ llm_chain = LLMChain(
     # verbose=True,
 )
 
-template_2 = """You are a friendly chat assistant called "CRETA" having a conversation with a human and you are created by Pachaiappan [portfolio](https://mr-vicky-01.github.io/Portfolio/) an AI Specialist.
+template_2 = """read the web_result and Answer the fallowing question carefully, your response must be short and informative
 web_result:
 {web_result}
-provided document:
-{provided_docs}
 previous_chat:
 {chat_history}
-Human: {human_input}
+Question: {human_input}
 Chatbot:"""
 
 
@@ -61,7 +59,21 @@ llm_chain_2 = LLMChain(
     verbose=True,
 )
 
+search_template = """Write a brief, user-friendly search query based on the details below. The response should be concise and ready for direct use on a search engine.
+Chat History:
+{chat_history}
+Question: {human_input}
+Search Query::"""
+
+search_prompt = PromptTemplate(
+    input_variables=["chat_history", "human_input"], template=search_template
+)
 
+search_llm = LLMChain(
+    llm=llm,
+    prompt=search_prompt,
+    # verbose=True,
+)
 
 previous_response = ""
 provided_docs = ""
@@ -74,8 +86,9 @@ def conversational_chat(query):
     result = llm_chain.predict(chat_history=previous_response, human_input=query, provided_docs=provided_docs)
 
     if  'search_query'in result.strip():
-        web_result = get_web_result(query)
-        print("searching on web....")
+        search_query = search_llm.predict(chat_history=previous_response, human_input=query)
+        print(search_query)
+        web_result = get_web_result(search_query.strip())
         result = llm_chain_2.predict(web_result= web_result,chat_history=previous_response, human_input=query, provided_docs=provided_docs)
 
     st.session_state['history'].append((query, result))
@@ -85,6 +98,7 @@ def conversational_chat(query):
     
     return result
 
+
 st.title("CRETA 🤖")
 st.text("I am CRETA Your Friendly Assitant")