app.py

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)