App.py

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

model_id = "sakthi54321/power_ai"

# Load model + tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

# Simple function: one input → one output
def ask_model(prompt):
    # force very direct answer
    input_text = f"Question: {prompt}\nAnswer:"
    
    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=800,   # keep answers short
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_p=0.9,
        temperature=0.7
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # only take the text after "Answer:"
    if "Answer:" in response:
        response = response.split("Answer:")[-1].strip()
    
    return response


# Gradio UI (straightforward)
demo = gr.Interface(
    fn=ask_model,
    inputs=gr.Textbox(label="Ask something", placeholder="Type your question here..."),
    outputs=gr.Textbox(label="Model Response"),
    title="🤖 Power AI",
    description="Straightforward Q&A with your trained model"
)

demo.launch()