3app.py

import flask
from flask import request, jsonify
from transformers import pipeline, AutoTokenizer # Added AutoTokenizer
import torch
import warnings 

# Suppress minor warnings that occur on CPU runs
warnings.filterwarnings("ignore")

app = flask.Flask(__name__)

# ===========================
# LOAD MODEL (SmolLM-1.7B-Chat)
# This model is small (1.7B) and fully open-access.
# ===========================
model_id = "HuggingFaceTB/SmolLM-1.7B" 
print("🔄 Loading model...")

# CPU/GPU device set
device = 0 if torch.cuda.is_available() else -1
# Use float32 for CPU (or bfloat16 for GPU)
dtype = torch.float32 if device == -1 else torch.bfloat16

try:
    # 1. Load Tokenizer and set pad_token for stability
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    if tokenizer.pad_token is None:
        # Set pad_token to eos_token to fix generation warning/error
        tokenizer.pad_token = tokenizer.eos_token 

    # 2. Load Pipeline with the fixed tokenizer
    ai = pipeline(
        "text-generation", 
        model=model_id, 
        tokenizer=tokenizer, # Passing the configured tokenizer here
        max_new_tokens=200, 
        device=device,
        torch_dtype=dtype, 
        trust_remote_code=True
    )
    print("✅ Model loaded!")
except Exception as e:
    print(f"❌ Error loading model: {e}")
    ai = None 

# ===========================
# CHAT API
# ===========================
@app.route('/chat', methods=['POST'])
def chat():
    if ai is None:
        return jsonify({"error": "Model initialization failed."}), 500
        
    try:
        data = request.get_json()
        msg = data.get("message", "")
        if not msg:
            return jsonify({"error": "No message sent"}), 400

        # Instruction Format: Using a simple template for this model
        prompt = f"User: {msg}\nAssistant:"
        
        output = ai(prompt)[0]["generated_text"]
        
        # Clean the output to extract only the model's reply
        # We split based on the 'Assistant:' tag in the prompt template
        if "Assistant:" in output:
             reply = output.split("Assistant:")[-1].strip()
        elif "User:" in output: # Sometimes the model repeats the prompt
             reply = output.split("User:")[0].strip()
        else:
             reply = output.strip()
             
        # Remove any remaining instruction markers from the start
        if reply.startswith(msg):
            reply = reply[len(msg):].strip()

        return jsonify({"reply": reply})
    except Exception as e:
        return jsonify({"error": str(e)}), 500

# ===========================
# RUN SERVER
# ===========================
if __name__ == "__main__":
    app.run(host='0.0.0.0', port=7860)