app.py

import gradio as gr
import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt
from transformers import AutoFeatureExtractor, AutoModelForImageClassification

# Use a smaller, more efficient model
model_name = "microsoft/resnet-18"  # Smaller model that should work with Hugging Face constraints

# Load model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Function to classify image
def classify_image(image):
    if image is None:
        return "No image provided", None
    
    try:
        # Process image
        inputs = feature_extractor(images=image, return_tensors="pt")
        
        # Make prediction
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
        
        # Get predicted class
        predicted_class_idx = logits.argmax(-1).item()
        predicted_class = model.config.id2label[predicted_class_idx]
        
        # Get top 5 predictions
        probs = torch.nn.functional.softmax(logits, dim=-1)[0]
        top5_prob, top5_indices = torch.topk(probs, 5)
        
        # Create plot for visualization
        fig, ax = plt.subplots(figsize=(10, 5))
        
        # Get class names and probabilities
        classes = [model.config.id2label[idx.item()] for idx in top5_indices]
        probabilities = [prob.item() * 100 for prob in top5_prob]
        
        # Create horizontal bar chart
        bars = ax.barh(classes, probabilities, color='#4C72B0')
        ax.set_xlabel('Probability (%)')
        ax.set_title('Top 5 Predictions')
        
        # Add percentage labels
        for i, bar in enumerate(bars):
            width = bar.get_width()
            ax.text(width + 1, bar.get_y() + bar.get_height()/2, 
                    f'{probabilities[i]:.1f}%', 
                    va='center', fontsize=10)
        
        # Improve layout
        plt.tight_layout()
        
        return predicted_class, fig
    
    except Exception as e:
        return f"Error: {str(e)}", None

# Create Gradio interface with simpler structure
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Textbox(label="Prediction"),
        gr.Plot(label="Confidence Levels")
    ],
    title="🖼️ Image Classification Tool",
    description="Upload an image to see what the AI recognizes in it!",
    allow_flagging="never",
    examples=[],  # No examples to avoid dependencies
    theme=gr.themes.Soft()
)

# Launch the app
if __name__ == "__main__":
    demo.launch()