app.py

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

# Choose the model you want to host
# You can replace with another like "TheBloke/meditron-7B-GPTQ" if you want faster performance
model_name = "microsoft/biogpt"


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

# Function that runs the model (inference)
def smart_health_predictor(prompt):
    # Add context to guide the model
    formatted_prompt = f"Question: {prompt}\nAnswer:"
    
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        repetition_penalty=1.2,
        eos_token_id=tokenizer.eos_token_id
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Remove the original question from the model's output
    if "Answer:" in response:
        response = response.split("Answer:")[-1].strip()
    
    return response


# Run the app
if __name__ == "__main__":
    app.launch()