import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# ✅ Choose a public model that is available on Hugging Face
MODEL_NAME = "mistralai/Mistral-7B-Instruct"  # Alternative: "microsoft/BioGPT-Large"

# ✅ Load the tokenizer and model
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
except Exception as e:
    print(f"Error loading model: {e}")
    model = None  # Prevents crashing if model doesn't load

def diagnose(symptoms):
    if model is None:
        return "⚠️ Error: AI model failed to load. Try again later."

    prompt = f"I have the following symptoms: {symptoms}. What could it be?"
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # ✅ Generate AI response
    output = model.generate(**inputs, max_length=200)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return response

# ✅ Create a simple web UI
interface = gr.Interface(
    fn=diagnose,
    inputs="text",
    outputs="text",
    title="AI Symptom Checker",
    description="Enter your symptoms, and the AI will suggest possible conditions."
)

# ✅ Launch the web app
if __name__ == "__main__":
    interface.launch()