app.py

# app.py (The Definitive Final Version)

import gradio as gr
from pathlib import Path
import asyncio
from PIL import Image

# Import backend components from the 'app' folder
from app.prediction import PredictionPipeline
from app.database import add_patient_record, get_all_records

# --- Initialization ---
prediction_pipeline = PredictionPipeline()
# Point to the locally cloned sample images directory from setup.sh
SAMPLE_IMAGE_DIR = Path("sample_images")
try:
    if SAMPLE_IMAGE_DIR.is_dir():
        # Separate the lists of images for the two-column layout
        NORMAL_SAMPLES = [str(p) for p in sorted(list((SAMPLE_IMAGE_DIR / 'NORMAL').glob('*.jpeg')))]
        PNEUMONIA_SAMPLES = [str(p) for p in sorted(list((SAMPLE_IMAGE_DIR / 'PNEUMONIA').glob('*.jpeg')))]
    else:
        raise FileNotFoundError
except FileNotFoundError:
    print("Warning: 'sample_images' directory not found or is empty. Please check setup.sh. Samples will be unavailable.")
    NORMAL_SAMPLES, PNEUMONIA_SAMPLES = [], []


# --- Core Logic (Async Functions) ---
async def process_analysis(patient_name, patient_age, image_list):
    """
    Handles the core logic: validates input, gets prediction, saves to DB, and returns UI updates.
    """
    # This function is now only for real analysis, not samples
    if not patient_name or patient_age is None:
        raise gr.Error("Patient Name and Age are required.")
    if not image_list:
        raise gr.Error("At least one image is required.")
    
    result = prediction_pipeline.predict(image_list)
    if "error" in result:
        raise gr.Error(result.get("details", result["error"]))

    final_pred = result["final_prediction"]
    final_conf = result["final_confidence"]
    
    # Save the record to the database
    await add_patient_record(str(patient_name), int(patient_age), final_pred, final_conf)

    confidences = {"NORMAL": 0.0, "PNEUMONIA": 0.0}
    confidences[final_pred] = final_conf
    # Calculate the other confidence score for the progress bar
    confidences["NORMAL" if final_pred == "PNEUMONIA" else "PNEUMONIA"] = 1 - final_conf
    
    # Return a list of updates for the output components
    return [
        gr.update(visible=False), # uploader_column
        gr.update(visible=True),  # results_column
        gr.update(value=result["watermarked_images"]), # result_images
        gr.update(value=confidences) # result_label
    ]

async def refresh_history_table():
    """Fetches records from the DB and formats them for the DataFrame."""
    records = await get_all_records()
    data_for_df = []
    if records:
        data_for_df = [[r.get('name'), r.get('age'), r.get('prediction_result'), f"{r.get('confidence_score', 0):.2%}", r.get('timestamp').strftime('%Y-%m-%d %H:%M')] for r in records]
    return gr.update(value=data_for_df)

# --- Gradio UI Definition ---
css = """
/* --- Professional Dark Theme & Fonts --- */
:root { --primary-hue: 220 !important; --secondary-hue: 210 !important; --neutral-hue: 210 !important; --body-background-fill: #111827 !important; --block-background-fill: #1F2337 !important; --block-border-width: 1px !important; --border-color-accent: #374151 !important; --background-fill-secondary: #1F2937 !important;}
/* --- Header & Title Styling --- */
#app_header { text-align: center; max-width: 900px; margin: 0 auto; }
#app_title { font-size: 3rem !important; font-weight: 800 !important; color: #FFFFFF !important; padding-top: 1rem; }
#app_subtitle { font-size: 1.25rem !important; color: #9CA3AF !important; margin-bottom: 2rem; }
/* --- Layout and Spacing --- */
#main_container { gap: 2rem; max-width: 700px; margin: 0 auto; }
#results_gallery .gallery-item { padding: 0.25rem !important; background-color: #374151; border: 1px solid #374151 !important; }
#bottom_controls { max-width: 500px; margin: 2.5rem auto 1rem auto; }
"""
with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue"), css=css, title="Pneumonia Detection AI") as demo:
    
    with gr.Column() as main_app:
        with gr.Column(elem_id="app_header"):
            gr.Markdown("# 🩺 Pneumonia Detection AI", elem_id="app_title")
            gr.Markdown("An AI-powered tool to assist in the diagnosis of pneumonia.", elem_id="app_subtitle")
        
        with gr.Row(elem_id="main_container"):
            with gr.Column(scale=1) as uploader_column:
                gr.Markdown("### Upload Patient X-Rays")
                image_input = gr.File(label="Upload up to 3 Images", file_count="multiple", file_types=["image"], type="filepath")
            
            with gr.Column(scale=2, visible=False) as results_column:
                gr.Markdown("### Analysis Results")
                result_images = gr.Gallery(label="Analyzed Images", columns=3, object_fit="contain", height=350, elem_id="results_gallery")
                result_label = gr.Label(label="Overall Prediction", num_top_classes=2)
                start_over_btn = gr.Button("Start New Analysis", variant="secondary")

        with gr.Group(visible=False) as patient_info_modal:
            gr.Markdown("## Enter Patient Details", elem_classes="text-center")
            patient_name_modal = gr.Textbox(label="Patient Name", placeholder="e.g., John Doe")
            patient_age_modal = gr.Number(label="Patient Age", minimum=0, maximum=120, step=1)
            with gr.Row():
                submit_analysis_btn = gr.Button("Analyze Images", variant="primary")
                cancel_btn = gr.Button("Cancel", variant="stop")
        
        with gr.Column(elem_id="bottom_controls"):
            with gr.Accordion("About this Tool", open=False):
                gr.Markdown(
                    """
                    ### MLOps-Powered Pneumonia Detection
                    This application demonstrates a complete, end-to-end MLOps pipeline for medical image classification. It leverages a state-of-the-art **Vision Transformer (ViT)** model, fine-tuned on a public dataset of chest X-ray images to distinguish between Normal and Pneumonia cases.
                    
                    **Disclaimer:** This tool is for demonstration and educational purposes only and is **not a substitute for professional medical advice.**

                    ---

                    **Project Team:**
                    *   **Alyyan Ahmed** - ML Engineer & Developer
                    *   **Munim Akbar** - ML Engineer & Developer
                    """
                )
            with gr.Row():
                samples_btn = gr.Button("Try Sample Images")
                history_btn = gr.Button("View Patient History")

    with gr.Column(visible=False) as history_page:
        gr.Markdown("# 📜 Patient Record History", elem_classes="app_title")
        with gr.Row():
            back_to_main_btn_hist = gr.Button("⬅️ Back to Main App")
            refresh_history_btn = gr.Button("Refresh History")
        history_df = gr.DataFrame(headers=["Name", "Age", "Prediction", "Confidence", "Date"], row_count=10, interactive=False)

    with gr.Column(visible=False) as samples_page:
        gr.Markdown("# 🖼️ Sample Image Library", elem_classes="app_title")
        gr.Markdown("You can download these sample images to test the tool on the main page.")
        back_to_main_btn_samp = gr.Button("⬅️ Back to Main App")
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("### Normal Cases")
                for img_path in NORMAL_SAMPLES:
                    gr.File(value=img_path, label=Path(img_path).name, interactive=False)
            
            with gr.Column():
                gr.Markdown("### Pneumonia Cases")
                for img_path in PNEUMONIA_SAMPLES:
                    gr.File(value=img_path, label=Path(img_path).name, interactive=False)
    
    # --- Event Handling Logic ---
    
    def show_patient_info(files):
        return gr.update(visible=True) if files else gr.update(visible=False)
    image_input.upload(fn=show_patient_info, inputs=image_input, outputs=patient_info_modal)

    async def submit_and_hide_modal(name, age, files):
        analysis_results = await process_analysis(name, age, files)
        return [*analysis_results, gr.update(visible=False)]
    submit_analysis_btn.click(fn=submit_and_hide_modal, inputs=[patient_name_modal, patient_age_modal, image_input], outputs=[uploader_column, results_column, result_images, result_label, patient_info_modal])
    
    cancel_btn.click(lambda: (gr.update(visible=False), None), None, [patient_info_modal, image_input])
    start_over_btn.click(fn=None, js="() => { window.location.reload(); }")
    
    all_pages = [main_app, history_page, samples_page]
    
    async def show_history_page_and_refresh():
        records_update = await refresh_history_table()
        return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), records_update]
    
    def show_samples_page():
        return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
    
    def show_main_page():
        return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]

    history_btn.click(fn=show_history_page_and_refresh, outputs=all_pages + [history_df])
    samples_btn.click(fn=show_samples_page, outputs=all_pages)
    back_to_main_btn_hist.click(fn=show_main_page, outputs=all_pages)
    back_to_main_btn_samp.click(fn=show_main_page, outputs=all_pages)
    
    refresh_history_btn.click(fn=refresh_history_table, outputs=history_df)
    demo.load(fn=refresh_history_table, outputs=history_df)

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