Update app.py

app.py

@@ -1,27 +1,35 @@
-# app.py (Final Version with Syntax Fix)
+# app.py (Final, Definitive, and Working Version)
 
 import gradio as gr
 from pathlib import Path
 import asyncio
-from PIL import Image
 
-# Import backend components
+# 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:
-    SAMPLE_IMAGES = [str(p) for p in sorted(list(SAMPLE_IMAGE_DIR.glob('*/*.jpeg')))]
-    if not SAMPLE_IMAGES:
+    # Ensure the directory exists and has images before creating the list
+    if SAMPLE_IMAGE_DIR.is_dir():
+        SAMPLE_IMAGES = [str(p) for p in sorted(list(SAMPLE_IMAGE_DIR.glob('*/*.jpeg')))]
+        if not SAMPLE_IMAGES:
+            print("Warning: 'sample_images' directory found, but it's empty.")
+    else:
         raise FileNotFoundError
 except FileNotFoundError:
-    print("Warning: 'sample_images' directory not found or is empty. Please check setup.sh.")
+    print("Warning: 'sample_images' directory not found. Please check setup.sh. Samples will be unavailable.")
     SAMPLE_IMAGES = []
 
-# --- Core Logic Functions ---
+# --- Core Logic (Async Functions) ---
 async def process_analysis(patient_name, patient_age, image_list, is_sample=False):
+    """
+    Handles the core logic: validates input, gets prediction, saves to DB, and returns UI updates.
+    """
     if not is_sample and (not patient_name or patient_age is None):
         raise gr.Error("Patient Name and Age are required.")
     if not image_list:
@@ -29,7 +37,7 @@ async def process_analysis(patient_name, patient_age, image_list, is_sample=Fals
     
     result = prediction_pipeline.predict(image_list)
     if "error" in result:
-        raise gr.Error(result["error"])
+        raise gr.Error(result.get("details", result["error"]))
 
     final_pred = result["final_prediction"]
     final_conf = result["final_confidence"]
@@ -49,9 +57,12 @@ async def process_analysis(patient_name, patient_age, image_list, is_sample=Fals
     ]
 
 async def refresh_history_table():
+    """Fetches records from the DB and formats them for the DataFrame."""
     records = await get_all_records()
-    data = [[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] if records else []
-    return gr.update(value=data)
+    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 = """
@@ -66,30 +77,28 @@ css = """
 #results_gallery .gallery-item { padding: 0.25rem !important; background-color: #374151; border: 1px solid #374151 !important; }
 #bottom_controls { max-width: 600px; margin: 2.5rem auto 1rem auto; }
 #bottom_controls .gr-accordion > .gr-block-label { text-align: center !important; display: block !important; }
-#sample_gallery .gallery-item { box-shadow: 0 0 5px rgba(0,0,0,0.5); border-radius: 8px !important; }
-#sample_gallery .gallery-item.selected { border: 4px solid var(--primary-500) !important; }
+/* --- Sample Gallery Selection Styling --- */
+#sample_gallery .gallery-item { box-shadow: 0 0 5px rgba(0,0,0,0.5); border-radius: 8px !important; border: 4px solid transparent; transition: border-color 0.3s ease; }
+#sample_gallery .gallery-item.selected { border-color: var(--primary-500) !important; }
 """
-
-# --- THIS IS THE FIX ---
-with gr.Blocks(
-    theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue"), 
-    css=css, 
-    title="Pneumonia Detection AI"
-) as demo:
+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")
@@ -97,20 +106,27 @@ with gr.Blocks(
             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
-                    (Your professional description here)
+                    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 & Munim Akbar
+
+                    **Project Team:**
+                    *   **Alyyan Ahmed** - Lead ML Engineer & Developer
+                    *   **Munim Akbar** - Project Contributor & Reviewer
                     """
                 )
             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():
@@ -121,32 +137,45 @@ with gr.Blocks(
     with gr.Column(visible=False) as samples_page:
         gr.Markdown("# 🖼️ Sample Image Library", elem_classes="app_title")
         gr.Markdown("Select up to 3 images by clicking on them, then click 'Analyze'.")
-        sample_gallery = gr.Gallery(
-            value=SAMPLE_IMAGES if SAMPLE_IMAGES else [],
-            label="Sample Images",
-            columns=5, height=400,
-            elem_id="sample_gallery"
-        )
-        selected_samples_textbox = gr.Textbox(visible=False, elem_id="selected_samples_textbox")
+        
+        sample_gallery = gr.Gallery(value=SAMPLE_IMAGES, label="Sample Images", columns=5, height=400, elem_id="sample_gallery")
+        
+        # This hidden textbox will store the list of selected file paths
+        selected_samples_textbox = gr.Textbox(label="selected", visible=False, elem_id="selected_samples_textbox")
+        
         with gr.Row():
             analyze_samples_btn = gr.Button("Analyze Selected Samples", variant="primary")
             back_to_main_btn_samp = gr.Button("⬅️ Back to Main App")
     
     # --- 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(); }")
+
+    # --- Sample Page Logic with JavaScript ---
     select_js = """
     (evt) => {
         const gallery = document.querySelector('#sample_gallery .grid-container');
-        const clicked_img = gallery.children[evt.index];
+        const clicked_img_container = gallery.children[evt.index];
         const selected_paths_input = document.querySelector('#selected_samples_textbox textarea');
-        let selected_paths = selected_paths_input.value ? selected_paths_input.value.split(',') : [];
-        const current_path = clicked_img.querySelector('img').alt;
-        if (clicked_img.classList.contains('selected')) {
-            clicked_img.classList.remove('selected');
+        let selected_paths = selected_paths_input.value ? selected_paths_input.value.split(',').filter(p => p.trim()) : [];
+        const current_path = clicked_img_container.querySelector('img').alt;
+
+        if (clicked_img_container.classList.contains('selected')) {
+            clicked_img_container.classList.remove('selected');
             selected_paths = selected_paths.filter(p => p !== current_path);
         } else {
             if (selected_paths.length < 3) {
-                clicked_img.classList.add('selected');
+                clicked_img_container.classList.add('selected');
                 selected_paths.push(current_path);
             } else {
                 alert("You can select a maximum of 3 images.");
@@ -155,37 +184,37 @@ with gr.Blocks(
         return selected_paths.join(',');
     }
     """
-    sample_gallery.select(fn=None, js=select_js, outputs=[selected_samples_textbox])
-    
-    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(); }")
+    sample_gallery.select(fn=None, _js=select_js, outputs=[selected_samples_textbox])
 
     async def handle_sample_analysis(selected_paths_str: str):
         selected_images = [path for path in selected_paths_str.split(',') if path]
-        if not selected_images: raise gr.Error("Please select at least one sample image.")
-        if len(selected_images) > 3: raise gr.Error("Please select no more than 3 sample images.")
+        if not selected_images:
+            raise gr.Error("Please select at least one sample image to analyze.")
         
         analysis_results = await process_analysis("Sample User", 0, selected_images, is_sample=True)
-        return [gr.update(visible=True), gr.update(visible=False), *analysis_results]
+        return [
+            gr.update(visible=True),   # main_app
+            gr.update(visible=False),  # samples_page
+            *analysis_results
+        ]
     analyze_samples_btn.click(fn=handle_sample_analysis, inputs=[selected_samples_textbox], outputs=[main_app, samples_page, uploader_column, results_column, result_images, result_label])
-
+    
+    # --- Page Navigation ---
     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)]
     
+    def show_samples_page():
+        # Also clear selections when navigating to the samples page
+        return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(value="")]
+    
+    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)
+    samples_btn.click(fn=show_samples_page, outputs=all_pages + [selected_samples_textbox])
     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)