app.py

from flask import Flask, request, jsonify, render_template
from transformers import AutoModelForCausalLM, AutoTokenizer

app = Flask(__name__)

# Charger le modèle et le tokenizer depuis Hugging Face
model_name = "salmapm/llama2_salma"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Définir une fonction de génération de texte
def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=50, num_return_sequences=1, temperature=0.7)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/generate', methods=['POST'])
def generate():
    data = request.json
    prompt = data.get('prompt', '')
    if prompt:
        generated_text = generate_text(prompt)
        return jsonify({"generated_text": generated_text})
    else:
        return jsonify({"error": "Veuillez entrer du texte."}), 400

if __name__ == '__main__':
    app.run(debug=True)