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import os
import requests
from transformers import pipeline
from flask import Flask, render_template, request, jsonify
app = Flask(__name__)
# Define model URL and local path
MODEL_URL = "https://huggingface.co/unsloth/Qwen3-4B-128K-GGUF/resolve/main/Qwen3-4B-128K-UD-IQ1_M.gguf"
MODEL_PATH = "Qwen3-4B-128K-UD-IQ1_M.gguf"
# Function to download the model
def download_model():
if not os.path.exists(MODEL_PATH):
print("Downloading the model...")
response = requests.get(MODEL_URL, stream=True)
with open(MODEL_PATH, 'wb') as model_file:
for chunk in response.iter_content(chunk_size=128):
model_file.write(chunk)
print("Model downloaded successfully.")
# Load the model with Hugging Face Transformers pipeline
def load_model():
download_model()
model = pipeline("text-generation", model=MODEL_PATH)
return model
# Global model object
model = load_model()
@app.route('/')
def index():
return render_template('index.html')
@app.route('/generate', methods=['POST'])
def generate():
user_input = request.form['message']
response = model(user_input, max_length=100)
return jsonify({"response": response[0]['generated_text']})
if __name__ == '__main__':
app.run(debug=True)
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