Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import pickle
+import cv2
+
+# Load model and class names
+model = tf.keras.models.load_model("model.h5")
+
+with open("class_names.pkl", "rb") as f:
+    class_names = pickle.load(f)
+
+# Image preprocessing function
+def preprocess_image(img):
+    img = cv2.resize(img, (224, 224))
+    img = img / 255.0
+    return np.expand_dims(img, axis=0)
+
+# Prediction function
+def predict(img):
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)  # Convert PIL image to OpenCV format
+    processed = preprocess_image(img)
+    prediction = model.predict(processed)[0]
+    predicted_label = class_names[np.argmax(prediction)]
+    confidence = float(np.max(prediction)) * 100
+    return f"Predicted: {predicted_label} ({confidence:.2f}%)"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload an Animal Image"),
+    outputs="text",
+    title="Animal Classifier with ResNet50",
+    description="Upload an image of an animal to classify using a pretrained ResNet50 model."
+)
+
+interface.launch()