Update app.py

app.py

@@ -7,6 +7,7 @@ from langchain.agents import initialize_agent, AgentType,Tool
 from langchain.schema import HumanMessage
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_openai import ChatOpenAI
+from langchain.prompts import PromptTemplate
 import gradio as gr
 import os
 import pytesseract
@@ -347,24 +348,25 @@ llm = ChatOpenAI(
     temperature=1,
     max_tokens=None,
     timeout=None,
-    max_retries=5,
-    prompt_template="""
-    You are JobAI.You process user input and generate human-like responses to assist with job searching and application processes. Tasks include:
-(provide links where ever required)
-
-- Searching for job openings based on user criteria
-- Providing job descriptions and requirements
-- Analyzing user resumes and offering suggestions for improvement
-- Generating cover letters tailored to specific job openings
-- Offering interview preparation assistance
-
-Respond to user queries and engage in conversation to guide them through the job application process. Utilize context and understanding to provide accurate and helpful responses
-   {{history}}
-   User Query: {{query}}
-   
-    """,
+    max_retries=5
 )
 
+prompt_template = PromptTemplate(
+    input_variables=["history", "query"],
+    template="""
+    You are JobAI. You process user input and generate human-like responses to assist with job searching and application processes. Tasks include:
+    (provide links where ever required)
+    - Searching for job openings based on user criteria
+    - Providing job descriptions and requirements
+    - Analyzing user resumes and offering suggestions for improvement
+    - Generating cover letters tailored to specific job openings
+    - Offering interview preparation assistance
+    Respond to user queries and engage in conversation to guide them through the job application process. Utilize context and understanding to provide accurate and helpful responses
+    {{history}}
+    
+    User Query: {{query}}
+    """
+)
 agent_tools = [ipc_tool, crpc_tool, doj_tool, faq_tool]
 
 agent = initialize_agent(
@@ -400,7 +402,7 @@ def chatbot_response(history,query):
         message = HumanMessage(content=[{"type": "text", "text": query}])
 
     # Invoke the model with the multimodal message
-    result = agent.invoke([history,message],handle_parsing_errors=True)
+    result = agent.invoke({'history':history,'input':message.context},handle_parsing_errors=True)
     response = result['output']
     intermediate_steps = result.get('intermediate_steps', [])