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from flask import Flask, request, jsonify
from transformers import pipeline
import torch
import os
app = Flask(__name__)
# Check for MPS (Apple Silicon) support
if torch.backends.mps.is_available():
device = torch.device("mps")
elif torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
print(f"Using device: {device}")
# Load the Hugging Face models
model_name = "./Qwen2.5-1.5B-Instruct-Local" # or any other model suitable for your needs
chatbot_model = pipeline("text-generation", model=model_name, device=device, batch_size=8)
# sentiment_model = pipeline("sentiment-analysis", device=device)
# Store conversation history
conversation_history = []
def generate_response(user_input, sentiment):
# if sentiment['label'] == 'NEGATIVE':
# prompt = f"The user is upset, respond with empathy and support: {user_input}"
# else:
# prompt = f"Respond to the following query: {user_input}"
# Append the user prompt to the conversation history
conversation_history.append({"role": "user", "content": prompt})
# Generate response from the model
result = chatbot_model(conversation_history, num_return_sequences=1, max_new_tokens=250)
# Append the assistant's response to the conversation history
conversation_history.append({"role": "assistant", "content": result[0]['generated_text'][-1]['content'] })
return result[0]['generated_text'][-1]['content']
@app.route('/chat', methods=['POST'])
def chat():
data = request.json
user_input = data.get('message', '')
if not user_input:
return jsonify({'error': 'Please provide a message.'}), 400
# sentiment = sentiment_model(user_input)[0]
response = generate_response(user_input, 'POSITIVE')
with open(file_path, 'w') as file:
file.write("\nConversation History:\n")
for item in conversation_history:
file.write(f"{item}\n")
return jsonify({'response': response , 'sentiment': sentiment})
@app.route('/history', methods=['GET'])
def get_history():
return jsonify(conversation_history)
if __name__ == '__main__':
# Create logs directory if it doesn't exist
file_path = "logs/conversation_history.txt"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
app.run(host='0.0.0.0', port=5500)
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