init

app.py

@@ -1,8 +1,70 @@
+# import streamlit as st
+# import os
+# from utils.demo import load_video, ctc_decode
+# from utils.two_stream_infer import load_model
+# import os
+# from scripts.extract_lip_coordinates import generate_lip_coordinates
+# import options as opt
+
+# st.set_page_config(layout="wide")
+
+# model = load_model()
+
+# st.title("Lipreading final year project Demo")
+
+# st.info(
+#     "The inference speed is very slow on Huggingface spaces due to it being processed entirely on CPU ",
+#     icon="ℹ️",
+# )
+# # Generating a list of options or videos
+# options = os.listdir(os.path.join("app_input"))
+# selected_video = st.selectbox("Choose video", options)
+
+# col1, col2 = st.columns(2)
+
+
+# with col1:
+#     file_path = os.path.join("app_input", selected_video)
+#     video_name = selected_video.split(".")[0]
+#     os.system(f"ffmpeg -i {file_path} -vcodec libx264 {video_name}.mp4 -y")
+
+#     # Rendering inside of the app
+#     video = open(f"{video_name}.mp4", "rb")
+#     video_bytes = video.read()
+#     st.video(video_bytes)
+
+
+# with col1, st.spinner("Splitting video into frames"):
+#     video, img_p, files = load_video(f"{video_name}.mp4", opt.device)
+#     prediction_video = video
+#     st.markdown(f"Frames Generated:\n{files}")
+#     frames_generated = True
+# with col1, st.spinner("Generating Lip Landmark Coordinates"):
+#     coordinates = generate_lip_coordinates(f"{video_name}_samples")
+#     prediction_coordinates = coordinates
+#     st.markdown(f"Coordinates Generated:\n{coordinates}")
+#     coordinates_generated = True
+
+# with col2:
+#     st.info("Ready to make prediction!")
+#     generate = st.button("Generate")
+#     if generate:
+#         with col2, st.spinner("Generating..."):
+#             y = model(
+#                 prediction_video[None, ...].to(opt.device),
+#                 prediction_coordinates[None, ...].to(opt.device),
+#             )
+#             txt = ctc_decode(y[0])
+#             st.text(txt[-1])
+            
+# st.info("Author ©️ : wissem karous ")
+# st.info("Made with ❤️  ")
+
+
 import streamlit as st
 import os
 from utils.demo import load_video, ctc_decode
 from utils.two_stream_infer import load_model
-import os
 from scripts.extract_lip_coordinates import generate_lip_coordinates
 import options as opt
 
@@ -16,46 +78,59 @@ st.info(
     "The inference speed is very slow on Huggingface spaces due to it being processed entirely on CPU ",
     icon="ℹ️",
 )
+
 # Generating a list of options or videos
-options = os.listdir(os.path.join("app_input"))
+options = sorted(os.listdir(os.path.join("app_input")))  # Ensure the list is sorted
 selected_video = st.selectbox("Choose video", options)
 
+# Find the index of the selected video and calculate the index of the next video
+selected_index = options.index(selected_video)
+next_video_index = (selected_index + 1) % len(options)  # Ensures looping back to start
+next_video = options[next_video_index]
+
 col1, col2 = st.columns(2)
 
+# Function to display video in a column
+def display_video(column, video_path, video_name):
+    os.system(f"ffmpeg -i {video_path} -vcodec libx264 {video_name}.mp4 -y")
+    video = open(f"{video_name}.mp4", "rb")
+    video_bytes = video.read()
+    column.video(video_bytes)
 
+# Displaying the selected video in the first column
 with col1:
     file_path = os.path.join("app_input", selected_video)
     video_name = selected_video.split(".")[0]
-    os.system(f"ffmpeg -i {file_path} -vcodec libx264 {video_name}.mp4 -y")
-
-    # Rendering inside of the app
-    video = open(f"{video_name}.mp4", "rb")
-    video_bytes = video.read()
-    st.video(video_bytes)
+    display_video(col1, file_path, video_name)
 
+# Displaying the next video in the second column
+with col2:
+    next_file_path = os.path.join("app_input", next_video)
+    next_video_name = next_video.split(".")[0]
+    display_video(col2, next_file_path, next_video_name)
 
-with col1, st.spinner("Splitting video into frames"):
+# Assuming further processing (like generating predictions) is only intended for the first (selected) video
+with col1, st.spinner("Processing video..."):
     video, img_p, files = load_video(f"{video_name}.mp4", opt.device)
-    prediction_video = video
-    st.markdown(f"Frames Generated:\n{files}")
-    frames_generated = True
-with col1, st.spinner("Generating Lip Landmark Coordinates"):
     coordinates = generate_lip_coordinates(f"{video_name}_samples")
-    prediction_coordinates = coordinates
-    st.markdown(f"Coordinates Generated:\n{coordinates}")
+    # Assuming 'frames_generated' and 'coordinates_generated' are used for control flow or further processing
+    frames_generated = True
     coordinates_generated = True
+    if frames_generated and coordinates_generated:
+        st.markdown(f"Frames Generated for {video_name}:\n{files}")
+        st.markdown(f"Coordinates Generated for {video_name}:\n{coordinates}")
 
 with col2:
     st.info("Ready to make prediction!")
     generate = st.button("Generate")
     if generate:
-        with col2, st.spinner("Generating..."):
+        with st.spinner("Generating..."):
             y = model(
-                prediction_video[None, ...].to(opt.device),
-                prediction_coordinates[None, ...].to(opt.device),
+                video[None, ...].to(opt.device),
+                coordinates[None, ...].to(opt.device),
             )
             txt = ctc_decode(y[0])
             st.text(txt[-1])
-            
-st.info("Author ©️ : wissem karous ")
-st.info("Made with ❤️  ")
+
+st.info("Author ©️ : Wissem Karous ")
+st.info("Made with ❤️")