app.py

import sys
import types

sys.modules["audioop"] = types.ModuleType("audioop")
sys.modules["pyaudioop"] = types.ModuleType("pyaudioop")

import os
import gradio as gr
from PIL import Image
from datasets import load_dataset
import model_utils
import visualization_utils

# --- Configuration ---
MODEL_NAME_OR_PATH = "google/vit-base-patch16-224-in21k"
DATASET_PATH = "pawlo2013/chest_xray"
MODEL_DIR = "./models"
EXAMPLES_FOLDER = "./examples"

# --- Load Data & Model ---
print("Loading dataset information...")
try:
    # We load the dataset mainly to get the class names correctly
    train_dataset = load_dataset(DATASET_PATH, split="train")
    class_names = train_dataset.features["label"].names
    print(f"Class names loaded: {class_names}")
except Exception as e:
    print(f"Warning: Could not load dataset, using default class names. Error: {e}")
    # Fallback class names based on typical chest X-ray classification
    class_names = ["NORMAL", "PNEUMONIA"]

print("Loading model and processor...")
try:
    model, processor = model_utils.load_model_and_processor(
        MODEL_DIR, MODEL_NAME_OR_PATH, class_names
    )
    print("Model and processor loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")
    raise e

# --- Core Logic ---
def classify_and_visualize(img, device="cpu"):
    if img is None:
        return None, None
    
    try:
        # Get predictions
        outputs, processed_input, probabilities, prediction_idx = model_utils.predict(
            model, processor, img, device
        )
        
        # Format probabilities
        result = {class_name: prob for class_name, prob in zip(class_names, probabilities)}
        
        # Generate heatmap
        heatmap_img = visualization_utils.show_final_layer_attention_maps(
            outputs, processed_input, device
        )
        
        return result, heatmap_img
    
    except Exception as e:
        print(f"Error in classification: {e}")
        # Return empty result and None for heatmap on error
        empty_result = {class_name: 0.0 for class_name in class_names}
        return empty_result, None

def format_output(img):
    try:
        probs, heatmap = classify_and_visualize(img)
        return probs, heatmap
    except Exception as e:
        print(f"Error in format_output: {e}")
        # Return empty results on error
        empty_result = {class_name: 0.0 for class_name in class_names}
        return empty_result, None

# --- Helper Functions ---
def load_examples_from_folder(folder_path):
    examples = []
    if os.path.exists(folder_path):
        for file in os.listdir(folder_path):
            if file.lower().endswith((".png", ".jpg", ".jpeg")):
                examples.append(os.path.join(folder_path, file))
    return examples

examples = load_examples_from_folder(EXAMPLES_FOLDER)

# --- UI Layout ---
title = "Pneumonia Detection Assistant"
description = """
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
    <p>Upload a Chest X-Ray image to analyze it for signs of Pneumonia.</p>
    <p>The model classifies the image into <b>Normal</b>, <b>Viral Pneumonia</b>, or <b>Bacterial Pneumonia</b> categories 
    and provides an attention heatmap to show which areas influenced the decision.</p>
</div>
"""
article = """
<div style="border: 2px solid #e74c3c; padding: 20px; border-radius: 10px; margin-top: 20px; background-color: #fce4e4;">
    <h3 style="color: #c0392b; margin-top: 0;">⚠️ MEDICAL DISCLAIMER</h3>
    <p style="color: #7f8c8d;">
        This application uses Artificial Intelligence (Vision Transformer) for educational and research purposes only. 
        <b>It is NOT a diagnostic tool.</b> The results generated by this model should not be treated as medical advice.
        Always consult with a qualified healthcare professional for medical diagnosis and treatment.
    </p>
</div>
"""

theme = gr.themes.Soft(
    primary_hue="blue",
    secondary_hue="slate",
).set(
    button_primary_background_fill="*primary_500",
    button_primary_background_fill_hover="*primary_600",
)

iface = gr.Interface(
    fn=format_output,
    inputs=gr.Image(type="pil", label="Upload Chest X-Ray"),
    outputs=[
        gr.Label(label="Prediction Confidence", num_top_classes=3),
        gr.Image(label="Attention Heatmap Analysis"),
    ],
    examples=examples,
    cache_examples=False,
    title=title,
    description=description,
    article=article,
    theme=theme,
    # allow_flagging="never"
)

# Launch
if __name__ == "__main__":
    iface.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=True
)