app.py

# app.py

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

# Load model and tokenizer from local directory
model_path = "./fine_tuned_model"  # You can change this if your model is in a subdirectory
device = "cuda" if torch.cuda.is_available() else "cpu"

print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
)

if device == "cuda":
    model.to(device)

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Gradio Interface
interface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=3, label="Your Question"),
    outputs=gr.Textbox(lines=10, label="Model Response"),
    title="Genius by OG",
    description="Ask a question and receive a response from Genius model."
)

# Launch the app (Hugging Face will call launch automatically)
interface.launch()