app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import random

# Configuration 🛠️
model_name = "microsoft/phi-3-mini-4k-instruct"  # Smaller model for memory constraints
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load model with optimizations
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
    device_map="auto",
    low_cpu_mem_usage=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Safety tools 🛡️
SAFE_RESPONSES = [
    "Let's focus on positive tech projects! 🌱",
    "How about designing an eco-friendly robot? 🤖",
    "Let's explore renewable energy solutions! ☀️"
]

def generate_response(message, history):
    # Simple safety check
    if any(word in message.lower() for word in ["violence", "hate", "gun"]):
        return random.choice(SAFE_RESPONSES)
    
    # Format prompt
    prompt = f"<|user|>\n{message}<|end|>\n<|assistant|>"
    
    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate response
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Decode and return
    return tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)

# Create Gradio interface
demo = gr.ChatInterface(
    fn=generate_response,
    examples=[
        "How to make a solar-powered robot?",
        "Python code for air quality sensor"
    ],
    title="🤖 REACT Ethical AI Lab",
    description="Safe AI project assistant for students"
)

# Explicit API setup
api = gr.mount_gradio_app(
    app=demo.app,
    blocks=demo,
    path="/api"
)

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        enable_queue=True,
        share=False
    )