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03afb93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | from flask import Flask, request, render_template
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
app = Flask(__name__)
# Load fine-tuned model and tokenizer
model_path = "./finetuned_weights"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
device = torch.device("cpu")
model.to(device)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/generate', methods=['POST'])
def generate():
user_input = request.form['prompt'].strip()
prompt = f"<|USER|> {user_input} <|ASSISTANT|> "
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = generated_text.split("<|ASSISTANT|> ")[-1] if "<|ASSISTANT|> " in generated_text else generated_text
return response
if __name__ == '__main__':
app.run(debug=True) |