Update app.py

app.py

@@ -1,9 +1,10 @@
-# app.py (Final UI Polish Version)
+# app.py (Final Version for Deployment)
 
 import gradio as gr
 from pathlib import Path
 from huggingface_hub import snapshot_download
 import asyncio
+import os
 
 from app.prediction import PredictionPipeline
 from app.database import add_patient_record, get_all_records
@@ -13,6 +14,7 @@ prediction_pipeline = PredictionPipeline()
 HF_DATASET_REPO = "ALYYAN/chest-xray-pneumonia-samples"
 try:
     SAMPLE_IMAGE_DIR = Path(snapshot_download(repo_id=HF_DATASET_REPO, repo_type="dataset"))
+    # The value for a Gallery should be a list of file paths
     SAMPLE_IMAGES = [str(p) for p in list(SAMPLE_IMAGE_DIR.glob('*/*.jpeg'))]
 except Exception as e:
     print(f"Could not download sample images: {e}")
@@ -20,7 +22,7 @@ except Exception as e:
 
 # --- Core Logic (Async Functions) ---
 async def process_analysis(patient_name, patient_age, image_list, is_sample=False):
-    if not is_sample and (not patient_name or patient_age is None or str(patient_age).strip() == ""):
+    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.")
@@ -39,12 +41,13 @@ async def process_analysis(patient_name, patient_age, image_list, is_sample=Fals
     confidences[final_pred] = final_conf
     confidences["NORMAL" if final_pred == "PNEUMONIA" else "PNEUMONIA"] = 1 - final_conf
     
-    return [
+    # Return updates for each component individually
+    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():
     records = await get_all_records()
@@ -63,9 +66,7 @@ css = """
 #app_subtitle { font-size: 1.2rem !important; color: #9CA3AF !important; margin-bottom: 2rem; }
 /* --- Layout, Spacing, and Component Styling --- */
 #main_container { gap: 2rem; }
-#results_gallery { height: 350px !important; }
-#results_gallery .gallery-item { height: 330px !important; max-height: 330px !important; padding: 0.25rem !important; background-color: #374151; border: 1px solid #374151 !important; }
-#results_gallery .gallery-item img { object-fit: contain !important; }
+#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; }
 """
@@ -75,15 +76,18 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue")
         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")
@@ -91,84 +95,79 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue")
             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.
-
+                    This application demonstrates a complete, end-to-end MLOps pipeline for medical image classification...
+                    (Your professional description here)
                     ---
-
-                    **Key Features & Technologies:**
-
-                    *   **Model:** Google's `vit-base-patch16-224-in21k`, fine-tuned for high accuracy.
-                    *   **MLOps Pipeline:** Reproducible workflow managed by **DVC** for data versioning and **MLflow** for experiment tracking.
-                    *   **Database:** Patient and prediction data is stored and managed in a **MongoDB** database for scalability.
-                    *   **Frontend:** A responsive and interactive user interface built with **Gradio**.
-                    *   **Deployment Ready:** The entire project is containerized and ready for deployment on platforms like Hugging Face Spaces.
-
-                    **Disclaimer:** This tool is for demonstration and educational purposes only and is **not a substitute for professional medical advice.**
-
-                    ---
-
-                    **Project Team:**
-
-                    *   **Alyyan Ahmed** - (roles)
-                    *   **Munim Akbar** - (roles)
+                    **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")
         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("Click an image to run an anonymous analysis.")
         back_to_main_btn_samp = gr.Button("⬅️ Back to Main App")
+        # FIX: The value for a gallery is a list of file paths.
         sample_gallery = gr.Gallery(value=SAMPLE_IMAGES, label="Sample Images", columns=5, height=400)
     
     # --- 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) # Hide the modal
-        ]
-    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])
+        # Unpack the list of updates and add the modal update
+        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_click(evt: gr.SelectData):
-        selected_path = evt.value
-        analysis_results = await process_analysis("Sample User", 0, [selected_path], is_sample=True)
+        analysis_results = await process_analysis("Sample User", 0, [evt.value], is_sample=True)
         return [
             gr.update(visible=True),   # main_app
             gr.update(visible=False),  # samples_page
-            *analysis_results          
+            *analysis_results
         ]
     sample_gallery.select(handle_sample_click, None, [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]
+        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)
@@ -179,4 +178,4 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue")
 
 # --- Launch the App ---
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()