Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load your trained TensorFlow face recognition model
+model = tf.keras.models.load_model(r"C:\Users\tiruv\Downloads\1.h5")
+
+# Map the predicted label to a class name
+class_names = {
+    0: "akilesh",
+    1: "aswath",
+    2: "bhuvan",
+    3: "karthikeyan",
+    4: "lalpradhap",
+    5: "muhilan",
+    6: "ragavan",
+    7: "sanjay",
+    8: "seenivas",
+    9: "sharvesh"
+}
+
+def predict_image(img):
+    if img is None:
+        return "No image provided"
+    
+    try:
+        # Preprocess the image
+        img = img.resize((224, 224))  # Ensure the size matches your training data
+        img_array = image.img_to_array(img)
+        img_array = tf.expand_dims(img_array, 0)  # Create a batch of size 1
+        
+        # Predict the class
+        predictions = model.predict(img_array)
+        predicted_class = np.argmax(predictions[0])
+        
+        # Map prediction to class name
+        predicted_class_name = class_names.get(predicted_class, "Unknown class")
+        
+        return predicted_class_name
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+gr.Interface(fn=predict_image,
+             inputs=gr.Image(type="pil"),  # Default configuration
+             outputs="text",
+             title="Image Classifier",
+             description="Upload an image to classify it").launch(share=True)