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app.py

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+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()

requirements.txt.txt

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+gradio==4.28.3        # For the app UI
+pillow                # For image processing (PIL)
+torch                 # For PyTorch-based image models
+torchvision           # Optional: for pretrained models like ResNet
+requests              # Useful for connecting to hosted APIs (e.g. Roboflow)
+transformers          # For Hugging Face models (image-classification pipeline)
+opencv-python         # Optional: for image pre-processing / GradCAM
+matplotlib            # Optional: for visual overlays like heatmaps