Upload main.py

main.py

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+# from flask import Flask, request, jsonify
+# from llama_cpp import Llama
+# from huggingface_hub import hf_hub_download
+# from model import model_download
+# # model_download()
+
+# # Initialize the Llama model with chat format set to "llama-2"
+# llm = Llama(model_path="E:\langchain-chat-gui-main\langchain-chat-gui-main\model-unsloth.Q8_0.gguf", chat_format="llama-2")
+
+# # Define the system prompt
+# system_prompt = (
+#     "[INSTRUCTION] You are a chatbot named 'Makkal Thunaivan' designed to provide legal support to marginalized communities in India. "
+#     "You were fine-tuned by Sathish Kumar and his team members at the University College of Engineering Dindigul. "
+#      "Developer Team members include Karthikeyan as Model Trainer, Prashanna as Dataset Researcher, Nivas as Model Architect, and Sathish Kumar as Team Leader and Frontend Developer and Model Tester. "
+#     "Your purpose is to answer questions related to Indian law and marginalized communities in India. "
+#     "You have been trained on various legal topics. "
+#     "Your responses should be concise, meaningful, and accurate."
+#     "When a user asks for more information or details, provide a more comprehensive explanation. "
+#     "Your responses should be respectful and informative."
+#     "Do not provide information unrelated to India or Indian law. "
+#     "Feel free to ask questions."
+# )
+
+# # Initialize the conversation history list with the system prompt
+# conversation_history = [{"role": "system", "content": system_prompt}]
+
+# # Create a Flask application
+# app = Flask(__name__)
+
+# # Define the model function
+# def model(query):
+#     global conversation_history  # Declare global to update history
+
+#     # Add the user's query to the conversation history
+#     conversation_history.append({"role": "user", "content": query})
+
+#     # Calculate the total number of tokens in the conversation history
+#     # (You may need to modify this part to calculate the token count accurately based on your tokenizer)
+#     total_tokens = sum(len(message["content"].split()) for message in conversation_history)
+
+#     # If the total number of tokens exceeds the model's context window, trim the history
+#     # You may need to adjust the 512 value based on your model's actual context window size
+#     context_window_size = 512
+#     while total_tokens > context_window_size:
+#         # Remove the oldest messages from the conversation history
+#         conversation_history.pop(0)
+#         # Recalculate the total number of tokens
+#         total_tokens = sum(len(message["content"].split()) for message in conversation_history)
+
+#     # Generate chat completion with the conversation history
+#     response = llm.create_chat_completion(messages=conversation_history, max_tokens=75)
+    
+#     # Extract the assistant's response from the completion dictionary
+#     if response and 'choices' in response and response['choices']:
+#         assistant_response = response['choices'][0]['message']['content']
+#         assistant_response = assistant_response.strip()
+        
+#         # Add the assistant's response to the conversation history
+#         conversation_history.append({"role": "assistant", "content": assistant_response})
+
+#         # Print the assistant's response
+#         print("Assistant response:", assistant_response)
+        
+#         # Return the assistant's response
+#         return assistant_response
+#     else:
+#         print("Error: Invalid response structure.")
+#         return None
+
+
+# # Define the endpoint for the API
+# @app.route("/chat", methods=["GET"])
+# def chat_endpoint():
+#     # Get the query parameter from the request
+#     query = request.args.get("query")
+    
+#     # Check if the "refresh" parameter is set to "true"
+#     refresh = request.args.get("refresh")
+#     if refresh and refresh.lower() == "true":
+#         # Clear the conversation history
+#         global conversation_history
+#         conversation_history = [{"role": "system", "content": system_prompt}]
+#         return jsonify({"response": "Conversation history cleared."})
+    
+#     # If there is no query, return an error message
+#     if not query:
+#         return jsonify({"error": "Query parameter is required."}), 400
+    
+#     # Call the model function with the query
+#     response = model(query)
+    
+#     # Return the assistant's response as JSON
+#     return jsonify({"response": response})
+
+# # Run the Flask app
+# if __name__ == "__main__":
+#     app.run(host="0.0.0.0", port=5000)
+
+
+
+from flask import Flask, request, jsonify
+from llama_cpp import Llama
+import logging
+
+# Initialize logging
+logging.basicConfig(level=logging.INFO)
+
+# Initialize the Llama model with chat format set to "llama-2"
+llm = Llama(model_path="E:\\langchain-chat-gui-main\\langchain-chat-gui-main\\llama-2-7b-chat.Q2_K.gguf", chat_format="llama-2")
+
+# Define the system prompt
+system_prompt = (
+    "[INSTRUCTION] You are a chatbot named 'Makkal Thunaivan' designed to provide legal support to marginalized communities in India. "
+    "You were fine-tuned by Sathish Kumar and his team members at the University College of Engineering Dindigul. "
+     "Developer Team members include Karthikeyan as Model Trainer, Prashanna as Dataset Researcher, Nivas as Model Architect, and Sathish Kumar as Team Leader and Frontend Developer and Model Tester. "
+    "Your purpose is to answer questions related to Indian law and marginalized communities in India. "
+    "You have been trained on various legal topics. "
+    "Your responses should be concise, meaningful, and accurate."
+    "When a user asks for more information or details, provide a more comprehensive explanation. "
+    "Your responses should be respectful and informative."
+    "Do not provide information unrelated to India or Indian law. "
+    "Feel free to ask questions."
+)
+
+# Initialize the conversation history list with the system prompt
+conversation_history = [{"role": "system", "content": system_prompt}]
+
+# Define conversation history size limit
+MAX_CONVERSATION_HISTORY_SIZE = 10
+
+# Create a Flask application
+app = Flask(__name__)
+
+# Define a function to calculate the total number of tokens in conversation history using the Llama model's tokenizer
+def calculate_total_tokens(messages):
+    try:
+        # Convert content to string and tokenize
+        total_tokens = sum(len(llm.tokenize(str(message["content"]), add_bos=False, special=True)) for message in messages)
+        return total_tokens
+    except Exception as e:
+        logging.error(f"Error during tokenization: {e}")
+        return 0  # Return a safe value (0) to handle the error
+
+# Define a function to trim the conversation history if the total number of tokens exceeds the context window size
+def trim_conversation_history():
+    global conversation_history
+    total_tokens = calculate_total_tokens(conversation_history)
+    context_window_size = 512
+
+    while total_tokens > context_window_size:
+        # Remove the oldest messages from the conversation history
+        conversation_history.pop(0)
+        # Recalculate the total number of tokens
+        total_tokens = calculate_total_tokens(conversation_history)
+
+# Define the model function
+def model(query):
+    global conversation_history
+
+    # Add the user's query to the conversation history
+    conversation_history.append({"role": "user", "content": query})
+
+    # Calculate the total number of tokens in the conversation history
+    total_tokens = calculate_total_tokens(conversation_history)
+
+    # If the total number of tokens exceeds the model's context window, trim the history
+    trim_conversation_history()
+
+    # Generate chat completion with the conversation history
+    try:
+        response = llm.create_chat_completion(messages=conversation_history, max_tokens=200)
+
+        # Extract the assistant's response from the completion dictionary
+        if response and 'choices' in response and response['choices']:
+            assistant_response = response['choices'][0]['message']['content']
+            assistant_response = assistant_response.strip()
+
+            # Add the assistant's response to the conversation history
+            conversation_history.append({"role": "assistant", "content": assistant_response})
+
+            # Return the assistant's response
+            return assistant_response
+        else:
+            logging.error("Error: Invalid response structure.")
+            return None
+    except Exception as e:
+        logging.error(f"Error during chat completion: {e}")
+        return None
+
+# Define the endpoint for the API
+@app.route("/chat", methods=["GET"])
+def chat_endpoint():
+    # Get the query parameter from the request
+    query = request.args.get("query")
+
+    # Check if the "refresh" parameter is set to "true"
+    refresh = request.args.get("refresh")
+    if refresh and refresh.lower() == "true":
+        # Clear the conversation history
+        global conversation_history
+        conversation_history = [{"role": "system", "content": system_prompt}]
+        return jsonify({"response": "Conversation history cleared."})
+
+    # If there is no query, return an error message
+    if not query:
+        return jsonify({"error": "Query parameter is required."}), 400
+
+    # Call the model function with the query
+    response = model(query)
+
+    # Return the assistant's response as JSON
+    if response is None:
+        return jsonify({"error": "An error occurred while processing the request."}), 500
+
+    # Check the size of the conversation history and clear if necessary
+    if len(conversation_history) > MAX_CONVERSATION_HISTORY_SIZE:
+        conversation_history = [{"role": "system", "content": system_prompt}]
+        return jsonify({"response": response, "notification": "Conversation history was cleared due to exceeding maximum size."})
+    print(response)
+    return jsonify({"response": response})
+
+# Run the Flask app
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)