Update app.py

app.py

@@ -1,31 +1,66 @@
 import gradio as gr
-from transformers import AutoModelForImageClassification, AutoFeatureExtractor
-from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
 
-# Load the model and feature extractor
-MODEL_NAME = "shaktibiplab/Animal-Classification"
-model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
-extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)
+# Load the trained model
+MODEL_PATH = "best_model.weights.h5"
+model = load_model(MODEL_PATH)
+
+# Define the class names
+class_names = [
+    "Bear", "Bird", "Cat", "Cow", "Deer", 
+    "Dog", "Dolphin", "Elephant", "Giraffe", 
+    "Horse", "Kangaroo", "Lion", "Panda", 
+    "Tiger", "Zebra"
+]
 
-# Function to classify uploaded image
 def classify_image(image):
-    image = Image.open(image).convert("RGB")
-    inputs = extractor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_class_idx = logits.argmax(-1).item()
-    return model.config.id2label[predicted_class_idx]
-
-# Create Gradio Interface
-interface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(type="file"),
-    outputs="text",
-    
-    title="Animal Classifier",
-    description="Upload an image of an animal, and the model will classify it into one of the trained categories."
-)
-
-# Launch the application
-if __name__ == "__main__":
-    interface.launch()
+    img = image.resize((256, 256))
+    img_array = img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+    return f"Predicted Class: {predicted_class}"
+
+def instruction():
+    return (
+        "**Important Note:**\n\n"
+        "This model is specifically trained to classify images into the following **15 animal categories**:\n\n"
+        "- Bear\n"
+        "- Bird\n"
+        "- Cat\n"
+        "- Cow\n"
+        "- Deer\n"
+        "- Dog\n"
+        "- Dolphin\n"
+        "- Elephant\n"
+        "- Giraffe\n"
+        "- Horse\n"
+        "- Kangaroo\n"
+        "- Lion\n"
+        "- Panda\n"
+        "- Tiger\n"
+        "- Zebra\n\n"
+        "**Usage Limitation:**\n\n"
+        "- The model will only recognize images containing these animals.\n"
+        "- Uploading an image of an animal not listed above or a non-animal image may result in inaccurate or undefined predictions.\n\n"
+        "Ensure the uploaded image is clear, contains a single animal, and resembles the categories listed for the best results."
+    )
+
+# Gradio Interface
+with gr.Blocks() as app:
+    gr.Markdown("# Animal Classifier")
+    gr.Markdown(instruction())
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload an Image", type="pil")
+            predict_button = gr.Button("Classify Image")
+        with gr.Column():
+            result_output = gr.Textbox(label="Prediction Result", lines=3)
+
+    predict_button.click(classify_image, inputs=image_input, outputs=result_output)
+
+app.launch()