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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+# Define image dimensions
+IMG_HEIGHT = 150
+IMG_WIDTH = 150
+
+# All 70 class names from the trained model
+class_names = [
+    'Algal Leaf Spot (Jackfruit)',
+    'Anthracnose (Mango)',
+    'Aphids (Cotton)',
+    'Apple scab (Apple)',
+    'Bacterial Blight (Cotton)',
+    'Bacterial Canker (Mango)',
+    'Bacterial Leaf Spot (Pumpkin)',
+    'Bacterial spot (Peach)',
+    'Bacterial spot (Pepper, bell)',
+    'Bacterial spot (Tomato)',
+    'BacterialBlights (Sugarcane)',
+    'Black Rot (Cauliflower)',
+    'Black Spot (Jackfruit)',
+    'Black rot (Apple)',
+    'Black rot (Grape)',
+    'BrownSpot (Rice)',
+    'Cedar apple rust (Apple)',
+    'Cercospora leaf spot Gray leaf spot (Corn (maize))',
+    'Common rust (Corn (maize))',
+    'Cutting Weevil (Mango)',
+    'Die Back (Mango)',
+    'Downy Mildew (Pumpkin)',
+    'Early blight (Potato)',
+    'Early blight (Tomato)',
+    'Esca (Black Measles) (Grape)',
+    'Gall Midge (Mango)',
+    'Haunglongbing (Citrus greening) (Orange)',
+    'Healthy (Cauliflower)',
+    'Healthy (Cotton)',
+    'Healthy (Jackfruit)',
+    'Healthy (Mango)',
+    'Healthy (Rice)',
+    'Healthy (Sugarcane)',
+    'Healthy Leaf (Pumpkin)',
+    'Hispa (Rice)',
+    'Late blight (Potato)',
+    'Late blight (Tomato)',
+    'Leaf Mold (Tomato)',
+    'Leaf blight (Isariopsis Leaf Spot) (Grape)',
+    'Leaf scorch (Strawberry)',
+    'LeafBlast (Rice)',
+    'Mosaic (Sugarcane)',
+    'Mosaic Disease (Pumpkin)',
+    'Northern Leaf Blight (Corn (maize))',
+    'Powdery Mildew (Cotton)',
+    'Powdery Mildew (Mango)',
+    'Powdery Mildew (Pumpkin)',
+    'Powdery mildew (Cherry (including sour))',
+    'RedRot (Sugarcane)',
+    'Rust (Sugarcane)',
+    'Septoria leaf spot (Tomato)',
+    'Sooty Mould (Mango)',
+    'Spider mites Two-spotted spider mite (Tomato)',
+    'Target Spot (Tomato)',
+    'Target spot (Cotton)',
+    'Tomato Yellow Leaf Curl Virus (Tomato)',
+    'Tomato mosaic virus (Tomato)',
+    'Unknown Disease',
+    'Yellow (Sugarcane)',
+    'healthy (Apple)',
+    'healthy (Blueberry)',
+    'healthy (Cherry (including sour))',
+    'healthy (Corn (maize))',
+    'healthy (Grape)',
+    'healthy (Peach)',
+    'healthy (Pepper, bell)',
+    'healthy (Potato)',
+    'healthy (Raspberry)',
+    'healthy (Soybean)',
+    'healthy (Strawberry)',
+    'healthy (Tomato)'
+]
+
+# Load the TensorFlow SavedModel
+print("Loading model...")
+print(f"Current directory: {os.getcwd()}")
+print(f"Files in current directory: {os.listdir('.')}")
+
+model = None
+infer = None
+
+try:
+    # Try different possible model paths
+    possible_paths = [
+        './plant_disease_savemodel',
+        './plant_disease_savedmodel',
+        'plant_disease_savemodel',
+        'plant_disease_savedmodel'
+    ]
+    
+    model_path = None
+    for path in possible_paths:
+        if os.path.exists(path):
+            model_path = path
+            print(f"Found model at: {model_path}")
+            break
+    
+    if model_path is None:
+        raise FileNotFoundError("Model directory not found. Please ensure 'plant_disease_savemodel' folder is uploaded.")
+    
+    # Check if model files exist
+    model_files = os.listdir(model_path)
+    print(f"Files in model directory: {model_files}")
+    
+    # Load the model
+    model = tf.saved_model.load(model_path)
+    infer = model.signatures["serving_default"]
+    print(f"✅ Model loaded successfully from {model_path}")
+    
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    import traceback
+    traceback.print_exc()
+    model = None
+    infer = None
+
+def predict_disease(image):
+    """
+    Predict plant disease from an image
+    
+    Args:
+        image: PIL Image or numpy array
+        
+    Returns:
+        dict: Dictionary with class names as keys and confidence scores as values
+        Format compatible with CropGuard mobile app
+    """
+    if model is None or infer is None:
+        return {
+            "Error": 1.0,
+            "Message": "Model not loaded. Please check the model files."
+        }
+    
+    try:
+        # Convert to PIL Image if needed
+        if isinstance(image, np.ndarray):
+            img = Image.fromarray(image.astype('uint8'), 'RGB')
+        else:
+            img = image
+        
+        # Ensure RGB mode
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+        
+        # Resize to model input size (150x150 as per training)
+        img = img.resize((IMG_WIDTH, IMG_HEIGHT))
+        
+        # Convert to array and normalize
+        img_array = np.array(img, dtype=np.float32)
+        img_array = img_array / 255.0  # Normalize to [0, 1]
+        
+        # Add batch dimension
+        img_array = np.expand_dims(img_array, axis=0)
+        
+        # Make prediction
+        predictions = infer(tf.constant(img_array))
+        
+        # Get the output tensor (try different possible keys)
+        if 'output_0' in predictions:
+            output = predictions['output_0'].numpy()
+        elif 'dense_1' in predictions:
+            output = predictions['dense_1'].numpy()
+        elif 'dense' in predictions:
+            output = predictions['dense'].numpy()
+        else:
+            # Use the first output
+            output = list(predictions.values())[0].numpy()
+        
+        # Get predictions for all classes
+        predictions_dict = {}
+        for i, class_name in enumerate(class_names):
+            if i < len(output[0]):
+                predictions_dict[class_name] = float(output[0][i])
+        
+        # Log top prediction for debugging
+        top_class = max(predictions_dict.items(), key=lambda x: x[1])
+        print(f"Top prediction: {top_class[0]} ({top_class[1]*100:.2f}%)")
+        
+        # Return in format compatible with Gradio Label output
+        # Gradio will automatically show top predictions
+        # Mobile app expects: { "class_name": confidence, ... }
+        return predictions_dict
+        
+    except Exception as e:
+        print(f"Prediction error: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return {
+            "Error": 1.0,
+            "Message": f"Prediction failed: {str(e)}"
+        }
+
+# Create Gradio interface
+title = "🌱 CropGuard Tech - Plant Disease Detection"
+description = """
+Upload an image of a plant leaf to detect diseases using AI.
+
+**Supported Crops:** Apple, Blueberry, Cauliflower, Cherry, Corn, Cotton, Grape, Jackfruit, Mango, Orange, Peach, Pepper, Potato, Pumpkin, Raspberry, Rice, Soybean, Strawberry, Sugarcane, Tomato
+
+**Model Specs:**
+- 70 disease classes
+- 95%+ accuracy
+- CNN architecture
+- Trained on 10,000+ images
+"""
+
+article = """
+### About CropGuard Tech
+This AI model was trained on Google Colab using a comprehensive plant disease dataset from Kaggle. 
+It can identify 70 different plant diseases across 19+ crop varieties.
+
+**Creator:** Beauttah Kipruto (Student at Elimu High School)
+
+**Model Repository:** [View on Hugging Face](https://huggingface.co/4lph4v3rs3/plant-disease-classification-model)
+"""
+
+examples = [
+    # You can add example images here if you have them
+]
+
+# Create the interface
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Image(type="pil", label="Upload Plant Leaf Image"),
+    outputs=gr.Label(num_top_classes=5, label="Disease Predictions"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    theme=gr.themes.Soft(),
+    allow_flagging="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+tensorflow-cpu==2.13.0
+gradio==4.16.0
+numpy==1.24.3
+Pillow==10.2.0