Update app.py

app.py

@@ -1,18 +1,16 @@
-# app.py (Final Version - No Downloads, Modern JS)
+# app.py (Final Version with Syntax Fix)
 
 import gradio as gr
 from pathlib import Path
 import asyncio
+from PIL import Image
 
+# Import backend components
 from app.prediction import PredictionPipeline
 from app.database import add_patient_record, get_all_records
 
 # --- Initialization ---
 prediction_pipeline = PredictionPipeline()
-
-# --- FIX 1: Remove Hugging Face Hub download logic ---
-# The setup.sh script already clones the 'sample_images' directory.
-# We just need to point to it.
 SAMPLE_IMAGE_DIR = Path("sample_images")
 try:
     SAMPLE_IMAGES = [str(p) for p in sorted(list(SAMPLE_IMAGE_DIR.glob('*/*.jpeg')))]
@@ -22,20 +20,35 @@ except FileNotFoundError:
     print("Warning: 'sample_images' directory not found or is empty. Please check setup.sh.")
     SAMPLE_IMAGES = []
 
-# --- Core Logic Functions (Unchanged and Correct) ---
+# --- Core Logic Functions ---
 async def process_analysis(patient_name, patient_age, image_list, is_sample=False):
-    # ... (code is the same)
-    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: raise gr.Error("At least one image is required.")
+    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:
+        raise gr.Error("At least one image is required.")
+    
     result = prediction_pipeline.predict(image_list)
-    if "error" in result: raise gr.Error(result["error"])
-    final_pred, final_conf = result["final_prediction"], result["final_confidence"]
-    if not is_sample: 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; confidences["NORMAL" if final_pred == "PNEUMONIA" else "PNEUMONIA"] = 1 - final_conf
-    return [gr.update(visible=False), gr.update(visible=True), gr.update(value=result["watermarked_images"]), gr.update(value=confidences)]
+    if "error" in result:
+        raise gr.Error(result["error"])
+
+    final_pred = result["final_prediction"]
+    final_conf = result["final_confidence"]
+    
+    if not is_sample:
+        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
+    confidences["NORMAL" if final_pred == "PNEUMONIA" else "PNEUMONIA"] = 1 - final_conf
+    
+    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():
-    # ... (code is the same)
     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)
@@ -53,50 +66,81 @@ 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; }
-/* --- FIX: Style the sample gallery for a cleaner look --- */
-#sample_gallery { background-color: transparent !important; border: none !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; }
 """
-with gr.Blocks(theme=gr.themes.Default(primary_hue="blue"), secondary_hue="blue"), css=css, title="Pneumonia Detection AI") as demo:
+
+# --- 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:
     
-    # ... (UI Layout is the same)
     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")
+                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")
+                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")
+            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("...")
-            with gr.Row(): samples_btn = gr.Button("Try Sample Images"); history_btn = gr.Button("View Patient History")
+            with gr.Accordion("About this Tool", open=False):
+                gr.Markdown(
+                    """
+                    ### MLOps-Powered Pneumonia Detection
+                    (Your professional description here)
+                    ---
+                    **Project Team:** Alyyan Ahmed & Munim Akbar
+                    """
+                )
+            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")
+        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("Select up to 3 images by clicking on them, then click 'Analyze'.")
-        sample_gallery = gr.Gallery(value=SAMPLE_IMAGES, label="Sample Images", columns=5, height=400, elem_id="sample_gallery")
+        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")
-        with gr.Row(): analyze_samples_btn = gr.Button("Analyze Selected Samples", variant="primary"); back_to_main_btn_samp = gr.Button("⬅️ Back to Main App")
+        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 ---
-
-    # --- FIX 2: Use the modern gr.js() function for custom JavaScript ---
+    
     select_js = """
     (evt) => {
-        // This JS code runs in the browser when a sample image is clicked.
-        // It's the same logic as before.
         const gallery = document.querySelector('#sample_gallery .grid-container');
         const clicked_img = 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');
             selected_paths = selected_paths.filter(p => p !== current_path);
@@ -108,51 +152,46 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue"), secondary_hue="blue"
                 alert("You can select a maximum of 3 images.");
             }
         }
-        
-        // The return value of a gr.js function is passed to the next .then()
         return selected_paths.join(',');
     }
     """
+    sample_gallery.select(fn=None, js=select_js, outputs=[selected_samples_textbox])
     
-    # Add the CSS for the selection border
-    demo.css += "#sample_gallery .gallery-item.selected { border: 4px solid var(--primary-500) !important; }"
-
-    # The modern way to link JS to an event:
-    sample_gallery.select(
-        fn=None, # No Python function runs on click
-        js=select_js, # The JS function to run
-        outputs=[selected_samples_textbox] # The JS function's return value updates this component
-    )
-
-    # ... (the rest of the event handlers are correct)
     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)]
+        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(); }")
 
     async def handle_sample_analysis(selected_paths_str: str):
-        selected_images = selected_paths_str.split(',') if selected_paths_str.strip() else []
+        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.")
+        
         analysis_results = await process_analysis("Sample User", 0, selected_images, is_sample=True)
         return [gr.update(visible=True), gr.update(visible=False), *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])
 
     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]
+    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()