Update app.py

app.py

@@ -30,12 +30,10 @@ def fetch_sdo_images(start_date, end_date, ident="0171", size="1024", tool="hmii
         frames = []
         current = start
         while current <= end:
-            # Format URL: https://sdo.gsfc.nasa.gov/assets/img/browse/YEAR/MONTH/DAY/DATE_IDENT_SIZE_TOOL.jpg
             date_str = current.strftime("%Y%m%d_%H%M%S")
             year, month, day = current.strftime("%Y"), current.strftime("%m"), current.strftime("%d")
             url = urljoin(base_url, f"{year}/{month}/{day}/{date_str}_{ident}_{size}_{tool}.jpg")
             
-            # Fetch image
             try:
                 response = requests.get(url, timeout=5)
                 if response.status_code == 200:
@@ -112,6 +110,15 @@ def create_gif(frames, output_path, duration=0.5):
     )
     return output_path
 
+def handle_fetch(start_date, end_date, ident, size, tool):
+    """Fetch SDO images and return frames for preview."""
+    frames, error = fetch_sdo_images(start_date, end_date, ident, size, tool)
+    if error:
+        return error, []
+    # Convert frames to PIL Images for preview
+    preview_frames = [Image.fromarray(frame) for frame in frames]
+    return "Fetched {} images successfully.".format(len(frames)), preview_frames
+
 def analyze_images(frames, lower_bound, upper_bound, param1, param2, center_tolerance, morph_iterations, min_rad, display_mode):
     """Analyze frames for concentric circles, highlighting growing series."""
     try:
@@ -164,31 +171,30 @@ def analyze_images(frames, lower_bound, upper_bound, param1, param2, center_tole
 
         # Prepare output based on display mode
         if display_mode == "All Frames":
-            for i, frame in enumerate(frames):
-                output_frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
-                if i + 1 in growing_frames:
-                    for c in all_circle_data:
-                        if c["frame"] == i + 1:
-                            cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)
-                            cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 0, 0), 2)
-                results.append(Image.fromarray(output_frame))
+            # Show pure, unprocessed frames
+            results = [Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)) for frame in frames]
         elif display_mode == "Detected Frames":
+            # Show only frames with detected circles
             for c in all_circle_data:
                 output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
-                cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)
+                cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)  # Green for detected
                 if c["frame"] in growing_frames:
-                    cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 0, 0), 2)
+                    cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 165, 0), 2)  # Orange for growing
                 results.append(Image.fromarray(output_frame))
         elif display_mode == "Both (Detected Replaces Original)":
+            # Show all frames, replacing detected frames with circles
             for i, frame in enumerate(frames):
-                output_frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
-                if i + 1 in growing_frames:
+                if i + 1 in [c["frame"] for c in all_circle_data]:
                     for c in all_circle_data:
                         if c["frame"] == i + 1:
                             output_frame = cv2.cvtColor(c["output_frame"], cv2.COLOR_GRAY2RGB)
-                            cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)
-                            cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 0, 0), 2)
-                results.append(Image.fromarray(output_frame))
+                            cv2.circle(output_frame, c["center"], c["radius"], (0, 255, 0), 2)  # Green
+                            if c["frame"] in growing_frames:
+                                cv2.circle(output_frame, c["center"], c["radius"] + 2, (255, 165, 0), 2)  # Orange
+                            results.append(Image.fromarray(output_frame))
+                            break
+                else:
+                    results.append(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)))
 
         # Generate report
         if all_circle_data:
@@ -217,19 +223,19 @@ def analyze_images(frames, lower_bound, upper_bound, param1, param2, center_tole
     except Exception as e:
         return f"Error during analysis: {str(e)}", [], None
 
-def process_input(gif_file, start_date, end_date, ident, size, tool, lower_bound, upper_bound, param1, param2, center_tolerance, morph_iterations, min_rad, display_mode):
+def process_input(gif_file, start_date, end_date, ident, size, tool, lower_bound, upper_bound, param1, param2, center_tolerance, morph_iterations, min_rad, display_mode, fetched_frames_state):
     """Process either uploaded GIF or fetched SDO images."""
     if gif_file:
         frames, error = extract_frames(gif_file.name)
         if error:
-            return error, [], None
+            return error, [], None, []
     else:
-        frames, error = fetch_sdo_images(start_date, end_date, ident, size, tool)
-        if error:
-            return error, [], None
+        frames = fetched_frames_state
+        if not frames:
+            return "No fetched frames available. Please fetch images first.", [], None, []
     
-    # Preview first frame if available
-    preview = Image.fromarray(frames[0]) if frames else None
+    # Preview all frames
+    preview = [Image.fromarray(frame) for frame in frames] if frames else []
     
     # Analyze frames
     report, results, gif_path = analyze_images(
@@ -242,10 +248,13 @@ def process_input(gif_file, start_date, end_date, ident, size, tool, lower_bound
 with gr.Blocks(title="Solar CME Detection") as demo:
     gr.Markdown("""
     # Solar CME Detection
-    Upload a GIF or specify a date range to fetch SDO images and detect concentric circles indicative of coronal mass ejections (CMEs). 
-    Green circles mark detected features; red circles highlight growing series (potential Earth-directed CMEs).
+    Upload a GIF or fetch SDO images by date range to detect concentric circles indicative of coronal mass ejections (CMEs). 
+    Green circles mark detected features; orange circles highlight growing series (potential Earth-directed CMEs).
     """)
     
+    # State to store fetched frames
+    fetched_frames_state = gr.State(value=[])
+    
     with gr.Row():
         with gr.Column():
             gr.Markdown("### Input Options")
@@ -255,6 +264,7 @@ with gr.Blocks(title="Solar CME Detection") as demo:
             ident = gr.Textbox(label="Image Identifier", value="0171")
             size = gr.Textbox(label="Image Size", value="1024")
             tool = gr.Textbox(label="Instrument", value="hmiigr")
+            fetch_button = gr.Button("Fetch Images from URL")
             
             gr.Markdown("### Analysis Parameters")
             lower_bound = gr.Slider(minimum=0, maximum=255, value=low_int, step=1, label="Lower Intensity Bound (0-255)")
@@ -275,15 +285,28 @@ with gr.Blocks(title="Solar CME Detection") as demo:
         with gr.Column():
             gr.Markdown("### Outputs")
             report = gr.Textbox(label="Analysis Report", lines=10)
-            preview = gr.Image(label="Input Preview (First Frame)")
-            gallery = gr.Gallery(label="Frames with Detected Circles (Green: Detected, Red: Growing Series)")
+            preview = gr.Gallery(label="Input Preview (All Frames)")
+            gallery = gr.Gallery(label="Frames with Detected Circles (Green: Detected, Orange: Growing Series)")
             gif_output = gr.File(label="Download Resulting GIF")
     
+    # Fetch button action
+    fetch_button.click(
+        fn=handle_fetch,
+        inputs=[start_date, end_date, ident, size, tool],
+        outputs=[report, preview],
+        _js="() => {return {fetched_frames_state: []}}"
+    ).then(
+        fn=lambda report, preview_frames: (preview_frames, report),
+        inputs=[report, preview],
+        outputs=[fetched_frames_state, report]
+    )
+
+    # Analyze button action
     analyze_button.click(
         fn=process_input,
         inputs=[
             gif_input, start_date, end_date, ident, size, tool,
-            lower_bound, upper_bound, param1, param2, center_tolerance, morph_iterations, min_rad, display_mode
+            lower_bound, upper_bound, param1, param2, center_tolerance, morph_iterations, min_rad, display_mode, fetched_frames_state
         ],
         outputs=[report, gallery, gif_output, preview]
     )