Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+# Class names mapping
+class_names = {
+    0: 'arduino',
+    1: 'battery',
+    2: 'DCmotor',
+    3: 'DHT-11',
+    4: 'ESP8266',
+    5: 'LCD',
+    6: 'Loadcell',
+    7: 'RFID',
+    8: 'Tiva',
+    9: 'Ultrasonic',
+    10: 'Bluetooth Module'
+}
+
+# Load the pre-trained model
+model = load_model("Electronic-Component-Detector.keras")
+
+# Function to predict image
+def predict_image(img):
+    img = img.convert("RGB")
+    img = img.resize((224, 224))
+    data = np.asarray(img)
+    data = data / 255.0
+    probs = model.predict(np.expand_dims(data, axis=0))
+    top_prob = probs.max()
+    top_pred = class_names[np.argmax(probs)]
+    return f"This is a {top_pred} with {top_prob * 100:.2f}% confidence."
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Electronic Component Detector",
+    description="Upload an image of an electronic component, and the model will classify it.",
+)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    interface.launch()