import gradio as gr
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np

# ✅ Load model once
model = load_model("model.keras")
labels = ["Healthy", "Early Blight", "Late Blight"]

# ✅ Prediction function
def predict(img):
    img = img.resize((128, 128))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0) / 255.0

    preds = model.predict(img_array)
    idx = np.argmax(preds[0])
    conf = np.max(preds[0]) * 100

    return {
        "label": labels[idx],
        "confidence": f"{conf:.2f}%"
    }

# ✅ UI Text
title = "🌿 DeepCrop: AI Potato Disease Detector"
description = """
Upload a **potato leaf** image below, and DeepCrop’s AI will identify whether it’s **Healthy**, **Early Blight**, or **Late Blight**.

---

### 🧠 About
DeepCrop is an AI-powered crop disease detection tool that helps farmers identify and manage plant health faster.

---
"""

# ✅ Gradio Interface (no examples)
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Potato Leaf"),
    outputs=gr.JSON(label="Prediction Result"),
    title=title,
    description=description,
    theme=gr.themes.Soft(primary_hue="green", secondary_hue="lime"),
)

if __name__ == "__main__":
    demo.launch(share=True)