update

app.py

@@ -1,36 +1,12 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image
 
-# Load the image classification pipeline from Hugging Face
-classifier = pipeline("image-classification", model="kaiku03/vit-base-1stGen-Pokemon-Images")
-
-def classify_image(image):
-    # Convert the image to RGB (if not already)
-    image = image.convert("RGB")
-    
-    # Use the classifier pipeline to classify the image
-    results = classifier(image)
-    
-    # Format the results to display in the interface
-    return {result['label']: result['score'] for result in results}
-
-# Example Pokémon images (URLs or local file paths)
-examples = [
-    ["https://raw.githubusercontent.com/Kaiku03/Pokemon-ViT/main/examples/1.jpg"],
-    ["https://raw.githubusercontent.com/Kaiku03/Pokemon-ViT/main/examples/2.jpg"],
-    ["https://raw.githubusercontent.com/Kaiku03/Pokemon-ViT/main/examples/3.jpg"]
-]
-
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=classify_image, 
-    inputs=gr.Image(type="pil"), 
-    outputs=gr.Label(num_top_classes=3),
-    title="Pokémon Classification",
-    description="Upload a Pokémon image to classify it using this model.",
-    examples=examples  # Add examples here
-)
-
-# Launch the interface on Hugging Face Spaces
-interface.launch()
+pipe = pipeline(task="image-classification", 
+                # model that can do 22k-category classification
+                model="microsoft/beit-base-patch16-224-pt22k-ft22k")
+gr.Interface.from_pipeline(pipe, 
+                           title="22k Image Classification",
+                           description="Object Recognition using Microsoft BEIT",
+#                           examples = ['wonder_cat.jpg', 'aki_dog.jpg',],
+                           article = "Author: <a href=\"https://huggingface.co/rowel\">Rowel Atienza</a>",
+                           ).launch()

requirements.txt

@@ -1,4 +1,2 @@
-gradio==3.27.0
-transformers==4.33.0
-Pillow==9.1.0
-TensorFlo==2.0
+torch
+transformers