Update app.py

app.py

@@ -1,28 +1,58 @@
+import gradio as gr
+from keras.preprocessing import image
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
+import numpy as np
 
-from gradio import Interface, Image, Label
-import tensorflow as tf
-# Load your TensorFlow model
-model = tf.keras.models.load_model("traffic.h5")
-
-# Define your class names if needed
-class_names = ['Asian-Green-Bee-Eater', 'Brown-Headed-Barbet', 'Cattle-Egret', 'Common-Kingfisher', 'Common-Myna', 'House-Crow', 'Indian-Grey-Hornbill', 'Indian-Peacock', 'Indian-Roller', 'White-Breasted-Kingfisher']
-
-Class_names=['15 kmph', '18', '19', '30 kmph', '4 weehler', '40 kmph', '5 kmp only', '50 kmph', '51', '52', '53', '56', '57', '60 kmph', '70 kmph', '8', '80 kmph', '9', 'bicycle', 'chemical caution', 'cycle lane', 'dead end', 'go a ahead', 'home zone', 'horn', 'left dent curve', 'left service road', 'no 4-wheeler', 'no entry', 'no horn', 'no left turn', 'no over cross', 'no right and left turn', 'no right turn', 'no stopping and standing', 'no u-turn', 'proceed straight or turn right', 'railway gate', 'railway station', 'right service road', 'right v-dent curve', 'ring road', 'school zone', 'series of bends', 'side road left', 'side road right', 'sidewalk intersection', 'sland', 'traffic lights', 'turn left', 'turn right', 'turn right or left', 'u-turn', 'uphill', 'v-cuts', 'working area', 'zebra-crossing']
-# Function to make predictions
-def classify_image(image):
-    # Preprocess the image
-    img = tf.image.resize(image, (224, 224))
-    img = tf.expand_dims(img, 0)  # Add batch dimension
-    # Make prediction
-    prediction = model.predict(img)
-    predicted_class = Class_names[prediction.argmax()]
-    return predicted_class
-
-# Gradio interface
-image = Image()  # Remove the `shape` argument
-label = Label()
-
-# Create interface
-interface = Interface(classify_image, image, label,
-                      title="traffic sign detection ",
-                      description="Upload an image of a traffic sign to classify its sign. ").launch()
+# Load the pre-trained model from the local path
+model_path = 'Leaf_name.h5'
+model = load_model(model_path)
+
+def predict_disease(image_file, model, all_labels):
+    """
+    Predict the disease from an image using the trained model.
+
+    Parameters:
+    - image_file: image, input image file
+    - model: Keras model, trained convolutional neural network
+    - all_labels: list, list of class labels
+
+    Returns:
+    - str, predicted class label
+    """
+    try:
+        # Load and preprocess the image
+        img = image.load_img(image_file, target_size=(256, 256))
+        img_array = img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        img_array = img_array / 255.0  # Normalize the image
+
+        # Predict the class
+        predictions = model.predict(img_array)
+        predicted_class = np.argmax(predictions[0])
+
+        # Return the class label
+        return all_labels[predicted_class]
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+# List of class labels
+all_labels = ['Corn-Common_rust', 'Potato-Early_blight', 'Tomato-Bacterial_spot']
+
+# Define the Gradio interface
+def gradio_predict(image_file):
+    return predict_disease(image_file, model, all_labels)
+
+# Create a Gradio interface
+gr_interface = gr.Interface(
+    fn=gradio_predict,  # Function to call for predictions
+    inputs=gr.Image(type="filepath"),  # Upload image as file path
+    outputs="text",  # Output will be the class label as text
+    title="Plant Disease Predictor",
+    description="Upload an image of a plant to predict the disease.",
+)
+
+# Launch the Gradio app
+gr_interface.launch()