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from PIL import Image
import requests
from io import BytesIO
# Load your model (you'll need to upload trained_modela.keras to your space)
model = tf.keras.models.load_model('trained_modela.keras')
class_name = ['Apple___Apple_scab',
'Apple___Black_rot',
'Apple___Cedar_apple_rust',
'Apple___healthy',
'Blueberry___healthy',
'Cherry_(including_sour)___Powdery_mildew',
'Cherry_(including_sour)___healthy',
'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
'Corn_(maize)___Common_rust_',
'Corn_(maize)___Northern_Leaf_Blight',
'Corn_(maize)___healthy',
'Grape___Black_rot',
'Grape___Esca_(Black_Measles)',
'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
'Grape___healthy',
'Orange___Haunglongbing_(Citrus_greening)',
'Peach___Bacterial_spot',
'Peach___healthy',
'Pepper,_bell___Bacterial_spot',
'Pepper,_bell___healthy',
'Potato___Early_blight',
'Potato___Late_blight',
'Potato___healthy',
'Raspberry___healthy',
'Soybean___healthy',
'Squash___Powdery_mildew',
'Strawberry___Leaf_scorch',
'Strawberry___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_Yellow_Leaf_Curl_Virus',
'Tomato___Tomato_mosaic_virus',
'Tomato___healthy']
def predict_disease(image):
"""
Predict plant disease from uploaded image
"""
try:
# Preprocess the image
image = image.resize((128, 128))
input_arr = tf.keras.preprocessing.image.img_to_array(image)
input_arr = np.array([input_arr]) # Convert single image to a batch
input_arr = input_arr / 255.0 # Normalize if your model expects it
# Make prediction
prediction = model.predict(input_arr)
result_index = np.argmax(prediction)
confidence = prediction[0][result_index]
# Get disease name
disease_name = class_name[result_index]
return f"Disease: {disease_name}\nConfidence: {confidence:.2%}"
except Exception as e:
return f"Error: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=predict_disease,
inputs=gr.Image(type="pil", label="Upload Plant Image"),
outputs=gr.Textbox(label="Prediction Result"),
title="Plant Disease Detection API",
description="Upload an image of a plant leaf to detect diseases",
examples=[
# You can add example images here
]
)
if __name__ == "__main__":
iface.launch() |