Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.utils import img_to_array,load_img
+from keras.models import load_model
+import numpy as np
+
+# Load the pre-trained model from the local path
+model_path = 'cherry.h5'
+model = load_model(model_path)  # Load the model here
+
+def predict_disease(image_file, model, all_labels):
+
+    try:
+        # Load and preprocess the image
+        img = load_img(image_file, target_size=(224, 224))  # Use load_img from tensorflow.keras.utils
+        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)  # Use the loaded model here
+        predicted_class = np.argmax(predictions[0])
+
+        # Get the predicted class label
+        predicted_label = all_labels[predicted_class]
+
+        # Print the predicted label to the console
+
+        if predicted_label=='Cherry Healthy':
+            predicted_label = predicted_label = """<h3 align="center">Cherry Healthy</h3><br><br>
+          <center>No need use Pesticides</center>"""
+        elif predicted_label=='Cherry Powdery Mildew':
+            predicted_label = """
+            <style>
+                      li{
+                        font-size: 15px;
+                        margin-left: 90px;
+                        margin-top: 15px;
+                        margin-bottom: 15px;
+                      }
+                      h4{
+                        font-size: 17px;
+                        margin-top: 15px;
+                      }
+                      h4:hover{
+                        cursor: pointer;
+                      }
+
+                      h3:hover{
+                        cursor: pointer;
+                        color: blue;
+                        transform: scale(1.3);
+                      }
+                      .note{
+                        text-align: center;
+                        font-size: 16px;
+                      }
+                      p{
+                        font-size: 13px;
+                        text-align: center;
+                      }
+
+                </style>
+                <h3><center><b>Cherry Powdery Mildew</b></center></h3>
+                <h4>PESTICIDES TO BE USED:</h4>
+                <ul>
+                    <li>1. Chlorothalonil (Daconil)</li>
+                    <li>2. Mancozeb (Dithane)</li>
+                    <li>3. Propiconazole</li>
+                    <li>4. Azoxystrobin (Heritage)</li>
+                    <li>5. Pyraclostrobin (Cabrio)</li>
+
+
+                </ul>
+                <p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
+                <p>Be sure to follow local regulations and guidelines for application</p>
+
+
+            """
+
+
+
+
+        else:
+          predicted_label = """<h3 align="center">Choose Correct image</h3><br><br>
+          """
+
+        return predicted_label
+
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+# List of class labels
+all_labels = [
+    'Cherry Powdery Mildew',
+    'Cherry Healthy'
+]
+
+# Define the Gradio interface
+def gradio_predict(image_file):
+    return predict_disease(image_file, model, all_labels)  # Pass the model to the function
+
+# 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="html",  # Output will be the class label as text
+    title="Cherry Disease Predictor",
+    description="Upload an image of a plant to predict the disease.",
+)
+
+# Launch the Gradio app
+gr_interface.launch(share=True)