import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model
model_name = "google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def diagnose(component, symptom):
    prompt = f"What could be the issue if the {component} is showing this symptom: {symptom}?"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Gradio UI
interface = gr.Interface(
    fn=diagnose,
    inputs=[
        gr.Dropdown(["Motor", "Battery", "Sensor", "Controller", "Actuator"], label="Component"),
        gr.Textbox(lines=2, placeholder="Describe the symptom (e.g., not moving, overheating)...", label="Symptom")
    ],
    outputs="text",
    title="Robot Fault Diagnosis (Gen AI)",
    description="Select a robot component and describe the symptom to get possible fault diagnosis using Generative AI."
)

interface.launch()