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README.md

@@ -1,13 +1,12 @@
----
-title: Gradio
-emoji: 🌍
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.36.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: PadiCare Gradio
+emoji: 🌾
+colorFrom: green
+colorTo: yellow
+sdk: gradio
+app_file: app.py
+---
+
+# PadiCare – Deteksi Penyakit Daun Padi (Gradio)
+
+Aplikasi Gradio untuk klasifikasi penyakit daun padi berbasis CNN (.h5).

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load model
+model = tf.keras.models.load_model("daun_padi_cnn_model.h5")
+
+# Label klasifikasi
+CLASS_NAMES = [
+    "Bacterial Leaf Blight", "Leaf Blast", "Leaf Scald",
+    "Brown Spot", "Narrow Brown Spot", "Healthy"
+]
+
+# Fungsi prediksi
+def predict_image(image: Image.Image):
+    image = image.convert("RGB").resize((150, 150))
+    img_array = np.expand_dims(np.array(image), axis=0).astype(np.float32) / 255.0
+    prediction = model.predict(img_array)[0]
+    predicted_index = int(np.argmax(prediction))
+    predicted_label = CLASS_NAMES[predicted_index]
+    confidence = float(np.max(prediction))
+    
+    return {
+        label: float(f"{prob:.4f}") for label, prob in zip(CLASS_NAMES, prediction)
+    }
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=6),
+    title="🌾 PadiCare – Deteksi Penyakit Daun Padi",
+    description="Unggah gambar daun padi, dan sistem akan mendeteksi jenis penyakitnya menggunakan model CNN."
+)
+
+# Jalankan interface
+interface.launch()

daun_padi_cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e23ad0922f5dd76f969dae18a211e0dc45b0e03d9b892fa5d288a5453a1d40a7
+size 41526680

requirements.txt

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+gradio
+tensorflow==2.16.1
+pillow
+numpy