Update app.py

app.py

@@ -8,11 +8,10 @@ from ultralytics import YOLO
 import time
 import numpy as np
 import imageio_ffmpeg as ffmpeg
-import base64
 
 # Page config first
 st.set_page_config(
-    page_title="Fire Watch: Fire and Smoke Detection with an AI Vision Model",
+    page_title="Fire Watch: AI model detection of fire and smoke",
     page_icon="🔥",
     layout="wide",
     initial_sidebar_state="expanded"
@@ -29,7 +28,7 @@ for key in ["processed_frames", "slider_value", "processed_video", "start_time"]
 # Sidebar
 with st.sidebar:
     st.header("Upload & Settings")
-    source_file = st.file_uploader("Upload image or video to be analyzed:", type=["jpg", "jpeg", "png", "bmp", "webp", "mp4"])
+    source_file = st.file_uploader("Upload image/video", type=["jpg", "jpeg", "png", "bmp", "webp", "mp4"])
     confidence = float(st.slider("Confidence Threshold", 10, 100, 20)) / 100
     fps_options = {
         "Original FPS": None,
@@ -41,52 +40,40 @@ with st.sidebar:
         "1 frame/30s": 0.0333
     }
     video_option = st.selectbox("Output Frame Rate", list(fps_options.keys()))
-    process_button = st.button("Detect fire")
+    process_button = st.button("Detect fire and smoke")
     progress_bar = st.progress(0)
     progress_text = st.empty()
     download_slot = st.empty()
 
 # Main page
-st.title("Fire Watch: AI-Powered Fire and Smoke Detection")
-
-# Display result images directly
+st.title("Fire Watch: AI-Powered Fire Detection")
 col1, col2 = st.columns(2)
 with col1:
-    fire_4a_url = "https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/Fire_4a.jpg"
-    st.image(fire_4a_url, use_column_width=True)
-
+    st.image("https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/Fire_1.jpeg", use_column_width=True)
 with col2:
-    fire_3a_url = "https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/Fire_3a.jpg"
-    st.image(fire_3a_url, use_column_width=True)
+    st.image("https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/Fire_3.png", use_column_width=True)
 
 st.markdown("""
-Early wildfire detection using YOLOv8 AI vision model. See detected results above and video examples below, or upload your own content!
-Click on video frames to load and play examples.
+Early wildfire detection using YOLOv8 AI vision model. See examples below or upload your own content!
 """)
 
-# Function to create simple video pair HTML
-
-
-if not source_file:
-    st.info("Please upload a file to begin.")
+# Example videos
+st.header("Example Results")
+for example in [("T1.mp4", "T2.mp4"), ("LA1.mp4", "LA2.mp4")]:
+    col1, col2 = st.columns(2)
+    orig_url = f"https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/{example[0]}"
+    proc_url = f"https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/{example[1]}"
+    orig_data = requests.get(orig_url).content
+    proc_data = requests.get(proc_url).content
+    with col1:
+        st.video(orig_data)
+    with col2:
+        st.video(proc_data)
 
 st.header("Your Results")
 result_cols = st.columns(2)
 viewer_slot = st.empty()
 
-# Example videos (LA before T)
-#st.header("Example Results")
-#examples = [
- #   ("LA Example", "LA1.mp4", "LA2.mp4"),
-  #  ("T Example", "T1.mp4", "T2.mp4")
-#]
-#for title, orig_file, proc_file in examples:
- #   st.subheader(title)
-  #  orig_url = f"https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/{orig_file}"
-   # proc_url = f"https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/{proc_file}"
-   # video_html = create_video_pair(orig_url, proc_url)
-   # st.markdown(video_html, unsafe_allow_html=True)
-
 # Load model
 try:
     model = YOLO(model_path)
@@ -119,9 +106,6 @@ if process_button and source_file and model:
         output_fps = fps_options[video_option] if fps_options[video_option] else orig_fps
         sample_interval = max(1, int(orig_fps / output_fps)) if output_fps else 1
         
-        # Set fixed output FPS to 2 (500ms per frame = 2 FPS)
-        fixed_output_fps = 2
-        
         st.session_state.start_time = time.time()
         frame_count = 0
         processed_count = 0
@@ -131,6 +115,7 @@ if process_button and source_file and model:
             if frame_count % sample_interval == 0:
                 res = model.predict(frame, conf=confidence)
                 processed_frame = res[0].plot()[:, :, ::-1]
+                # Ensure frame is C-contiguous
                 if not processed_frame.flags['C_CONTIGUOUS']:
                     processed_frame = np.ascontiguousarray(processed_frame)
                 st.session_state.processed_frames.append(processed_frame)
@@ -139,17 +124,16 @@ if process_button and source_file and model:
                 elapsed = time.time() - st.session_state.start_time
                 progress = frame_count / total_frames
                 
-                if elapsed > 0 and progress > 0:
-                    total_estimated_time = elapsed / progress
-                    eta = total_estimated_time - elapsed
-                    elapsed_str = f"{int(elapsed // 60)}m {int(elapsed % 60)}s"
-                    eta_str = f"{int(eta // 60)}m {int(eta % 60)}s" if eta > 0 else "Almost done"
+                if elapsed > 0 and processed_count > 0:
+                    time_per_frame = elapsed / processed_count
+                    frames_left = (total_frames - frame_count) / sample_interval
+                    eta = frames_left * time_per_frame
+                    eta_str = f"{int(eta // 60)}m {int(eta % 60)}s"
                 else:
-                    elapsed_str = "0s"
                     eta_str = "Calculating..."
                 
                 progress_bar.progress(min(progress, 1.0))
-                progress_text.text(f"Progress: {progress:.1%}\nElapsed: {elapsed_str}\nETA: {eta_str}")
+                progress_text.text(f"Progress: {progress:.1%} | ETA: {eta_str}")
             
             frame_count += 1
             success, frame = vidcap.read()
@@ -162,7 +146,7 @@ if process_button and source_file and model:
             writer = ffmpeg.write_frames(
                 out_path,
                 (width, height),
-                fps=fixed_output_fps,  # Fixed at 2 FPS (500ms per frame)
+                fps=output_fps or orig_fps,
                 codec='libx264',
                 pix_fmt_in='bgr24',
                 pix_fmt_out='yuv420p'
@@ -177,10 +161,8 @@ if process_button and source_file and model:
                 st.session_state.processed_video = f.read()
             os.unlink(out_path)
             
-            elapsed_final = time.time() - st.session_state.start_time
-            elapsed_final_str = f"{int(elapsed_final // 60)}m {int(elapsed_final % 60)}s"
             progress_bar.progress(1.0)
-            progress_text.text(f"Progress: 100%\nElapsed: {elapsed_final_str}\nETA: 0m 0s")
+            progress_text.text("Processing complete!")
             with result_cols[0]:
                 st.video(source_file)
             with result_cols[1]:
@@ -188,7 +170,7 @@ if process_button and source_file and model:
             download_slot.download_button(
                 label="Download Processed Video",
                 data=st.session_state.processed_video,
-                file_name="results_fire_analysis.mp4",
+                file_name="processed_wildfire.mp4",
                 mime="video/mp4"
             )