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.gitattributes

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+efficientnet_model.keras filter=lfs diff=lfs merge=lfs -text

README.md

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----
-title: Codeblock
-emoji: 🏆
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍃 Plant Disease Detector
+
+This is a Gradio demo of a fruit and leaf disease classification model trained using EfficientNetB0.
+
+## 🧠 Model
+- Architecture: EfficientNetB0
+- Input Size: 160x160
+- Classes: 21 different fruit and leaf disease types
+- Trained using TensorFlow / Keras
+- Accuracy: ~99.5% (train), ~98.5% (val)
+
+## 🖼️ Usage
+Upload an image of a fruit or leaf, and the model will predict its disease type.
+
+## 🧪 Example Images
+You can use sample images provided in the `examples/` folder.
+
+## 🔧 Requirements
+All dependencies are listed in `requirements.txt`.
+
+## 🙋‍♂️ Author
+**Aarzoo Singh**, Research Intern  
+B.Tech CSE, NIT Patna

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+model = tf.keras.models.load_model("efficientnet_final_model.keras")
+
+CLASS_NAMES = [
+    "Pomegranate__diseased", "mango_Sooty Mould", "mango_Powdery Mildew", 
+    "mango_Healthy", "mango_Gall Midge", "mango_Die Back", 
+    "mango_Cutting Weevil", "mango_Bacterial Canker", "mango_Anthracnose",
+    "guava_Healthy", "guava_Red Rust", "guava_Sooty Mould", 
+    "guava_Algal Leaf Spot", "guava_Rust", "lime_Greening", 
+    "lime_Canker", "lime_Healthy", "lime_Die Back", 
+    "lime_Scab", "lime_Anthracnose", "lime_Sooty Mould"
+]
+
+def predict_disease(img):
+    img = img.resize((160, 160))
+    img_array = image.img_to_array(img)
+    img_array = preprocess_input(img_array)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)[0]
+    top_idx = np.argmax(prediction)
+    confidence = prediction[top_idx] * 100
+    label = CLASS_NAMES[top_idx]
+
+    return f"{label} ({confidence:.2f}%)"
+
+interface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Fruit Leaf Disease Classifier 🌿",
+    description="Upload an image of a fruit/leaf and the model will classify the disease type.",
+    examples=[
+        ["examples/Phytopthora.jpg"],
+        ["examples/RedRust.jpg"],
+        ["examples/HealthyMangoLeaf.jpg"],
+        ["examples/LimeLeafSpotted.jpg"]
+    ]
+)
+
+if __name__ == "__main__":
+    interface.launch()

efficientnet_model.keras

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+size 28183873

examples.zip

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+oid sha256:4b0871b2f0d7040dc6f139362f5cc6fded0771c34da386a4208705d5ca1a06d3
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requirements.txt

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+gradio
+tensorflow
+numpy
+pillow