Update app.py

app.py

@@ -30,25 +30,46 @@ from langchain.llms import OpenAI
 chain = load_qa_chain(OpenAI(), chain_type="stuff")
 import gradio as gr
 
+template = """Meet Serene, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Serene's goal is to assist you with any questions or problems you might have regarding Schizophrenia. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
+{chat_history}
+User: {user_message}
+Chatbot:"""
 
-# Define the function to process the query, retrieve documents, and run the chain process
+# Define prompt template
+prompt = PromptTemplate(
+    input_variables=["chat_history", "user_message"], template=template
+)
+
+# Initialize conversation memory
+memory = ConversationBufferMemory(memory_key="chat_history")
+
+# Define function to process user query
 def process_query(query):
+    # Retrieve conversation history
+    chat_history = memory.get()
+    
+    # Apply prompt template
+    input_prompt = prompt.render(chat_history=chat_history, user_message=query)
+    
     # Perform similarity search to retrieve relevant documents
     docs = document_search.similarity_search(query)
     
-    # Run the chain process with the retrieved documents and the user query
-    response = chain.run(input_documents=docs, question=query)
+    # Run question-answering process with retrieved documents and user query
+    response = chain.run(input_documents=docs, question=input_prompt)
+    
+    # Add user query to conversation history
+    memory.add(query)
     
     # Return the response
     return response
 
-# Define the Gradio interface
+# Define Gradio interface
 iface = gr.Interface(
-    fn=process_query,  # Function to process user input
-    inputs="text",  # Textbox for user input
-    outputs="text",  # Textbox to display the response
-    title="Document Search and Chain Process",  # Interface title
-    description="Enter your query to search for relevant documents and run the chain process."  # Interface description
+    fn=process_query,
+    inputs="text",
+    outputs="text",
+    title="Hey People!",
+    description="Hi! How can I assist you?"
 )
 
 # Launch the interface