Update app.py

app.py

@@ -11,7 +11,10 @@ import os
 import pytesseract
 from PIL import Image
 import pickle
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain.agents import AgentType, initialize_agent
+from langchain.memory import ConversationBufferMemory
+from langchain.llms import HuggingFaceHub
+from langchain.tools import Tool
 # Load the CSV data as a DataFrame
 df = pd.read_csv("hf://datasets/kshitij230/Indian-Law/Indian-Law.csv")
 model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
@@ -335,41 +338,51 @@ faq_tool=Tool(
     func=retrieve_faq,
     description="Provides Answers to commonly asked questions related to query keyword(s)"
 )
-llm = HuggingFaceEndpoint(
-    model="meta-llama/Meta-Llama-3-8B",
-    max_tokens=None,
-    timeout=None,
-    max_retries=2,
-    prompt_template="""
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferMemory
+
+prompt = PromptTemplate(
+    template="""
     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. 
     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.
-    Respond to user queries and engage in conversation te resolve their query.
-
+    Respond to user queries and engage in conversation to resolve their query.
 
     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:{{history}}
 
-    User: {{query}}
+    History: {history}
+
+    User: {query}
 
     Response:
-    """
+    """,
+    input_variables=["history", "query"]
 )
 
-agent_tools = [ipc_tool,crpc_tool,doj_tool,faq_tool]
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Meta-Llama-3-8B",
+    task="text-generation",
+    timeout=None
+)
+
+llm_chain = LLMChain(prompt=prompt, llm=llm, memory=ConversationBufferMemory())
+
+agent_tools = [ipc_tool, crpc_tool, doj_tool, faq_tool]
 
 agent = initialize_agent(
     tools=agent_tools,
-    llm=llm,
+    llm=llm_chain,
     agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
     verbose=True,
     return_intermediate_steps=True,
-    handle_parsing_errors=True,
+    handle_parsing_errors=True
 )
+
+
 def encode_image_to_base64(image_path):
     return pytesseract.image_to_string(Image.open(image_path))
 def chatbot_response(history,query):
@@ -393,7 +406,7 @@ 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({'query':message.content},handle_parsing_errors=True)
     response = result['output']
     intermediate_steps = result.get('intermediate_steps', [])