Update app.py

app.py

@@ -101,10 +101,8 @@ def doj_info(q):
     MINISTRY OF LAW AND JUSTICE
 DEPARTMENT OF JUSTICE
 ----
-Ministry of Law and Justice is the oldest limb of the Government of
-India. The Ministry functions through three integral departments -
-Department of Legal Affairs, Legislative Department and the
-Department of Justice.
+Ministry of Law and Justice is the oldest limb of the Government of India. The Ministry functions through three integral departments -
+Department of Legal Affairs, Legislative Department and the Department of Justice.
 VISION OF THE DEPARTMENT OF JUSTICE
 Facilitating administration of Justice that ensures easy access and
 timely delivery of Justice to all
@@ -317,7 +315,7 @@ Peoples’ Prosecution Service of the People’s Republic of China
 relating to promotion of cooperation in Legal/Judicial matters was
 signed on 23rd June, 2003.
 --------
-""".replace('/n','')
+""".replace('/n',' ')
 
 ipc_tool = Tool(
     name="IPC Information Retrieval",
@@ -351,18 +349,18 @@ llm = ChatOpenAI(
     max_retries=5
 )
 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
-    
-    
-    User Query: {{query}}
-    """
+   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.
+
+History: {}
+
+User: {}
+
+Response:
+"""
 
 agent_tools = [ipc_tool, crpc_tool, doj_tool, faq_tool]
 
@@ -378,8 +376,16 @@ agent = initialize_agent(
 
 def encode_image_to_base64(image_path):
     return pytesseract.image_to_string(Image.open(image_path))
+def parse(history):
+    p='\n'
+    if history==[]:
+        return "No Chat till now"
+    for i in history:
+        p+=i['role']+': '+i['content']+'\n'
+    return p
 def chatbot_response(history,query):
-    print(history)
+    l=parse(history)
+    
     if query.get('files'):
         # Encode image to base64
         image_data=""
@@ -389,15 +395,14 @@ def chatbot_response(history,query):
         # Create a multimodal message with both text and image data
         message = HumanMessage(
             content=[
-                {"type": "text", "text": template.format(query['text'] +" System :Image(s) was added to this prompt by this user. Text Extracted from this image (Some words may be misspelled ,Use your understanding ):"+image_data)},  # Add text input
+                {"type": "text", "text": template.format(l,query['text'] +" System :Image(s) was added to this prompt by this user. Text Extracted from this image (Some words may be misspelled ,Use your understanding ):"+image_data)},  # Add text input
                
             ]
         )
         #k+=" System :Image(s) was added to this prompt by this user. Text Extracted from this image (Some words may be misspelled ,Use your understanding ):"+image_data
     else:
         # If no image, only pass the text
-        message = HumanMessage(content=[{"type": "text", "text": template.format(query)}])
-
+        message = HumanMessage(content=[{"type": "text", "text": template.format(l,query)}])
     # Invoke the model with the multimodal message
     result = agent.invoke([message],handle_parsing_errors=True)
     response = result['output']