Uploaded deployment file

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+import cv2
+
+# Load the trained model
+model_path = 'C:/Users/kamel/Documents/Image Classification/model_checkpoint_manual_effnet.h5'
+model = load_model(model_path)
+
+# Define a function to preprocess the input image
+def preprocess_image(img):
+    # Check if img is a file path or an image object
+    if isinstance(img, str):
+        # Load and preprocess the image
+        img = cv2.imread(img)
+        img = cv2.resize(img, (224, 224))
+        img = img / 255.0  # Normalize pixel values
+        img = np.expand_dims(img, axis=0)  # Add batch dimension
+    elif isinstance(img, np.ndarray):
+        # If img is already an image array, resize it
+        img = cv2.resize(img, (224, 224))
+        img = img / 255.0  # Normalize pixel values
+        img = np.expand_dims(img, axis=0)  # Add batch dimension
+    else:
+        raise ValueError("Unsupported input type. Please provide a file path or a NumPy array.")
+
+    return img
+
+# Define the classification function
+def classify_image(img):
+    # Preprocess the image
+    img = preprocess_image(img)
+    
+    # Make predictions
+    predictions = model.predict(img)
+    
+    # Get the predicted class label
+    predicted_class = np.argmax(predictions)
+    
+    return f"Predicted Class: {predicted_class}"
+
+# Create a Gradio interface
+iface = gr.Interface(fn=classify_image, 
+                     inputs="image",
+                     outputs="text",
+                     live=True)
+
+# Launch the Gradio app
+iface.launch()
+
+
+# In[ ]:
+
+
+
+