Update app.py

app.py

@@ -97,22 +97,26 @@ llm = ChatGoogleGenerativeAI(
     max_tokens=None,
     timeout=None,
     max_retries=2,
-    prompt_template="""
+)
+prompt="""
     You are a highly specialized legal assistant with deep knowledge of the Indian Penal Code (IPC). 
     Your primary task is to retrieve and summarize legal information accurately from the IPC.pdf document provided to you. 
+    Keep the conversation Topic related to Department of Justice
     Your responses should be highly specific, fact-based, and free from any speculation or hallucinations. 
     Always cite the exact section from the IPC when providing an answer. 
     If the information is not available in the document, clearly state that and do not make any assumptions.
+    
+
 
     Example task: "What is the punishment for theft according to the IPC?"
     Example response: "According to Section 379 of the IPC, the punishment for theft is imprisonment of either description for a term which may extend to three years, or with fine, or with both."
+    
+    History:{{}}
 
-    Task: {{query}}
+    Task: {{}}
 
     Response:
-    """,
-)
-
+    """
 agent_tools = [ipc_tool,crpc_tool]
 
 agent = initialize_agent(
@@ -125,7 +129,8 @@ agent = initialize_agent(
 )
 def encode_image_to_base64(image_path):
     return pytesseract.image_to_string(Image.open(image_path))
-def chatbot_response(query):
+def chatbot_response(m,query):
+    
     if query.get('files'):
         # Encode image to base64
         image_data=""
@@ -144,7 +149,7 @@ def chatbot_response(query):
         message = HumanMessage(content=[{"type": "text", "text": query}])
 
     # Invoke the model with the multimodal message
-    result = agent.invoke([message])
+    result = agent.invoke(prompt.format(m,message))
     response = result['output']
     intermediate_steps = result.get('intermediate_steps', [])
     
@@ -158,7 +163,7 @@ def chatbot_response(query):
 # Step 5: Gradio Interface
 from gradio import ChatMessage
 def chatbot_interface(messages,prompt):
-    response, thought_process = chatbot_response(prompt)
+    response, thought_process = chatbot_response(messages,prompt)
     #messages.append(ChatMessage(role="user", content=prompt))
     
     for x in prompt["files"]: