app.py

import torch
import os
from PIL import Image
from transformers import AutoModelForImageClassification, SiglipImageProcessor
import gradio as gr
import pytesseract

# Model path
MODEL_PATH = "./model"

try:
    print(f"=== Loading model from: {MODEL_PATH} ===")
    print(f"Available files: {os.listdir(MODEL_PATH)}")
    
    # Load the model (this should work with your files)
    print("Loading model...")
    model = AutoModelForImageClassification.from_pretrained(MODEL_PATH, local_files_only=True)
    print("✅ Model loaded successfully!")
    
    # Load just the image processor (not the full AutoProcessor)
    print("Loading image processor...")
    try:
        # Try to load the image processor from your local files
        processor = SiglipImageProcessor.from_pretrained(MODEL_PATH, local_files_only=True)
        print("✅ Image processor loaded from local files!")
    except Exception as e:
        print(f"⚠️  Could not load local processor: {e}")
        print("Loading image processor from base SigLIP model...")
        # Fallback: load processor from base model online
        processor = SiglipImageProcessor.from_pretrained("google/siglip-base-patch16-224")
        print("✅ Image processor loaded from base model!")
    
    # Get labels from your model config
    if hasattr(model.config, 'id2label') and model.config.id2label:
        labels = model.config.id2label
        print(f"✅ Found {len(labels)} labels in model config")
    else:
        # Create generic labels if none exist
        num_labels = model.config.num_labels if hasattr(model.config, 'num_labels') else 2
        labels = {i: f"class_{i}" for i in range(num_labels)}
        print(f"✅ Created {len(labels)} generic labels")
    
    print("🎉 Model setup complete!")
    
except Exception as e:
    print(f"❌ Error loading model: {e}")
    print("\n=== Debug Information ===")
    print(f"Files in model directory: {os.listdir(MODEL_PATH)}")
    raise

def classify_meme(image: Image.Image):
    """
    Classify meme and extract text using OCR
    """
    try:
        # OCR: extract text from image
        extracted_text = pytesseract.image_to_string(image)
        
        # Process image for the model
        inputs = processor(images=image, return_tensors="pt")
        
        # Run inference
        with torch.no_grad():
            outputs = model(**inputs)
            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        
        # Get predictions
        predictions = {}
        for i in range(len(labels)):
            label = labels.get(i, f"class_{i}")
            predictions[label] = float(probs[0][i])
        
        # Sort predictions by confidence
        sorted_predictions = dict(sorted(predictions.items(), key=lambda x: x[1], reverse=True))
        
        # Debug prints
        print("=== Classification Results ===")
        print(f"Extracted Text: '{extracted_text.strip()}'")
        print("Top 3 Predictions:")
        for i, (label, prob) in enumerate(list(sorted_predictions.items())[:3]):
            print(f"  {i+1}. {label}: {prob:.4f}")
        
        return sorted_predictions, extracted_text.strip()
        
    except Exception as e:
        error_msg = f"Error processing image: {str(e)}"
        print(f"❌ {error_msg}")
        return {"Error": 1.0}, error_msg

# Create Gradio interface
demo = gr.Interface(
    fn=classify_meme,
    inputs=gr.Image(type="pil", label="Upload Meme Image"),
    outputs=[
        gr.Label(num_top_classes=5, label="Meme Classification"),
        gr.Textbox(label="Extracted Text", lines=3)
    ],
    title="🎭 Meme Classifier with OCR",
    description="""
    Upload a meme image to:
    1. **Classify** its content using your trained SigLIP2_77 model
    2. **Extract text** using OCR (Optical Character Recognition)
    
    Your model was trained on meme data and will predict the category/sentiment of the uploaded meme.
    """,
    examples=None,
    allow_flagging="never"
)

if __name__ == "__main__":
    print("🚀 Starting Gradio interface...")
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )