| import os |
| import numpy as np |
| import tensorflow as tf |
| import gradio as gr |
|
|
| |
| model_path = 'plantvillage_efficientnet_b0.keras' |
|
|
| if os.path.exists(model_path): |
| try: |
| model = tf.keras.models.load_model(model_path, compile=False) |
| print("Model loaded successfully!") |
| except Exception as e: |
| print(f"Error loading model: {e}") |
| model = None |
| else: |
| print(f"File not found: {model_path}") |
| model = None |
|
|
| classes = [ |
| 'Pepper__bell___Bacterial_spot', 'Pepper__bell___healthy', 'Potato___Early_blight', |
| 'Potato___Late_blight', 'Potato___healthy', 'Tomato_Bacterial_spot', |
| 'Tomato_Early_blight', 'Tomato_Late_blight', 'Tomato_Leaf_Mold', |
| 'Tomato_Septoria_leaf_spot', 'Tomato_Spider_mites_Two_spotted_spider_mite', |
| 'Tomato__Target_Spot', 'Tomato__Tomato_YellowLeaf__Curl_Virus', |
| 'Tomato__Tomato_mosaic_virus', 'Tomato_healthy' |
| ] |
|
|
| def predict(image): |
| if model is None: |
| raise gr.Error("Model is not loaded. Check model file path in your Space.") |
| |
| if image is None: |
| return {} |
|
|
| try: |
| |
| img = tf.image.resize(image, (224, 224)) |
| img = tf.cast(img, tf.float32) |
| img = tf.keras.applications.efficientnet.preprocess_input(img) |
| img = tf.expand_dims(img, axis=0) |
|
|
| |
| preds = model.predict(img, verbose=0)[0] |
| |
| |
| return {classes[i]: float(preds[i]) for i in range(len(classes))} |
|
|
| except Exception as err: |
| print(f"Prediction Error: {err}") |
| raise gr.Error(f"Prediction error: {str(err)}") |
|
|
| demo = gr.Interface( |
| fn=predict, |
| inputs=gr.Image(type="numpy", label="Upload Leaf Image"), |
| outputs=gr.Label(num_top_classes=3, label="Predictions"), |
| title="Plant Disease Detector", |
| description="Upload a crop leaf image to identify diseases." |
| ) |
|
|
| if __name__ == '__main__': |
| demo.launch() |
