Delete main.py

main.py

@@ -1,91 +0,0 @@
-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="./llama-2-7b-chat.Q2_K.gguf", chat_format="llama-2")
-
-# Define the system prompt
-system_prompt = (
-    "I am an Indian law chatbot designed to provide legal support to marginalized communities. "
-    "This model was fine-tuned by Sathish and his team members at the University College of Engineering Dindigul. "
-    "The model has been trained on various legal topics. "
-    "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)