checed

.gitignore

@@ -1 +1 @@
-class_indices.json
+plant_disease_model.tflite

app.py

@@ -1,14 +1,56 @@
+import os
+import json
+from PIL import Image
+import numpy as np
+import tensorflow as tf
 import gradio as gr
 
-def say_hello(name):
-    return f"Hello {name}, the model is ready on Hugging Face Spaces!"
+# Paths
+working_dir = os.path.dirname(os.path.abspath(__file__))
+model_path = f"{working_dir}/plant_disease_model.tflite"
 
-demo = gr.Interface(
-    fn=say_hello,
-    inputs="text",
-    outputs="text",
-    title="Test Space"
+# Load TFLite model
+interpreter = tf.lite.Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Load class indices
+class_indices = json.load(open(f"{working_dir}/class_indices.json"))
+
+# Function to preprocess the image
+def load_and_preprocess_image(image, target_size=(224, 224)):
+    img = image.resize(target_size)
+    img_array = np.array(img, dtype=np.float32)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    return img_array
+
+# Prediction function
+def predict_image_class(image):
+    preprocessed_img = load_and_preprocess_image(image)
+    
+    # Set input tensor
+    interpreter.set_tensor(input_details[0]['index'], preprocessed_img)
+    interpreter.invoke()
+    
+    # Get predictions
+    predictions = interpreter.get_tensor(output_details[0]['index'])
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    predicted_class_name = class_indices[str(predicted_class_index)]
+    
+    return f"Prediction: {predicted_class_name}"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_image_class,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="🌱 Plant Disease Classifier (TFLite)",
+    description="Upload a plant leaf image to classify its disease using a compressed TFLite model."
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()

class_indices.json

@@ -0,0 +1 @@
+{"0": "Apple___Apple_scab", "1": "Apple___Black_rot", "2": "Apple___Cedar_apple_rust", "3": "Apple___healthy", "4": "Blueberry___healthy", "5": "Cherry_(including_sour)___Powdery_mildew", "6": "Cherry_(including_sour)___healthy", "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "8": "Corn_(maize)___Common_rust_", "9": "Corn_(maize)___Northern_Leaf_Blight", "10": "Corn_(maize)___healthy", "11": "Grape___Black_rot", "12": "Grape___Esca_(Black_Measles)", "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "14": "Grape___healthy", "15": "Orange___Haunglongbing_(Citrus_greening)", "16": "Peach___Bacterial_spot", "17": "Peach___healthy", "18": "Pepper,_bell___Bacterial_spot", "19": "Pepper,_bell___healthy", "20": "Potato___Early_blight", "21": "Potato___Late_blight", "22": "Potato___healthy", "23": "Raspberry___healthy", "24": "Soybean___healthy", "25": "Squash___Powdery_mildew", "26": "Strawberry___Leaf_scorch", "27": "Strawberry___healthy", "28": "Tomato___Bacterial_spot", "29": "Tomato___Early_blight", "30": "Tomato___Late_blight", "31": "Tomato___Leaf_Mold", "32": "Tomato___Septoria_leaf_spot", "33": "Tomato___Spider_mites Two-spotted_spider_mite", "34": "Tomato___Target_Spot", "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "36": "Tomato___Tomato_mosaic_virus", "37": "Tomato___healthy"}