app.py

import gradio as gr
from PIL import Image
import random

# 💡 Placeholder classifier — replace with actual model or API call
def classify_mushroom(image):
    # Simulate prediction
    prediction = random.choice(["Edible", "Poisonous"])
    confidence = round(random.uniform(0.7, 0.99), 2)

    bilingual_map = {
        "Edible": "กินได้",
        "Poisonous": "พิษ"
    }

    return {
        "Prediction (EN)": prediction,
        "คำทำนาย (TH)": bilingual_map[prediction],
        "Confidence": f"{confidence * 100:.1f}%"
    }

# 🧪 Gradio App UI
with gr.Blocks() as demo:
    gr.Markdown("## 🍄 Mushroom Safety Classifier")
    gr.Markdown("Upload a photo of a mushroom to check if it's edible or poisonous.\nอัปโหลดรูปเห็ดเพื่อทำนายว่าเห็ดกินได้หรือมีพิษ")

    with gr.Row():
        image_input = gr.Image(type="pil", label="📷 Upload Mushroom Image")
        with gr.Column():
            label_en = gr.Textbox(label="🧠 Prediction (English)")
            label_th = gr.Textbox(label="🗣️ คำทำนาย (ภาษาไทย)")
            confidence = gr.Textbox(label="📶 Confidence Score")

    classify_btn = gr.Button("🔍 Classify")

    # 🔁 Connect button to function
    classify_btn.click(
        fn=classify_mushroom,
        inputs=image_input,
        outputs=[label_en, label_th, confidence]
    )

demo.launch()