Spaces:
Sleeping
Sleeping
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from flask import Flask, render_template, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
import datetime
app = Flask(__name__)
# Load the model and tokenizer
model_path = "phamhai/Llama-3.2-3B-Instruct-Frog"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
# Store chat history
chat_history = []
@app.route('/')
def index():
return render_template('index.html', history=chat_history)
@app.route('/chat', methods=['POST'])
def chat():
user_message = request.json['message']
# Append user message to chat history
timestamp = datetime.datetime.now().strftime("%H:%M:%S")
chat_history.append({'timestamp': timestamp, 'user': user_message})
# Generate a response
input_text = user_message # Modify this as needed for your model's input
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs, max_new_tokens=128)
bot_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Append bot response to chat history
chat_history.append({'timestamp': timestamp, 'bot': bot_response})
return jsonify({'timestamp': timestamp, 'response': bot_response})
@app.route('/clear', methods=['POST'])
def clear():
global chat_history
chat_history = [] # Clear chat history
return jsonify(success=True)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860) # Set host and port for Hugging Face Spaces
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