pages.py

import streamlit as st
from streamlit import session_state as sst
from utils import navigate_to

from model_inference import rate_video_frames,get_text_from_audio, summarize_from_text
from utils import read_important_frames, extract_audio


# Define size limits (adjust based on your system)
SMALL_VIDEO_LIMIT_MB = 35  # Files ≤ 35MB are small
LARGE_VIDEO_LIMIT_MB = 50  # Max large file upload allowed

# Convert MB to bytes
SMALL_VIDEO_LIMIT_BYTES = SMALL_VIDEO_LIMIT_MB * 1024 * 1024
LARGE_VIDEO_LIMIT_BYTES = LARGE_VIDEO_LIMIT_MB * 1024 * 1024

async def landing_page():

    uploaded_file = st.file_uploader("Upload a video",
                                     type=["mp4", "avi", "mov"])

    if uploaded_file is not None:
        file_size = uploaded_file.size  # Get file size in bytes

        # Restrict max file upload size
        if file_size > LARGE_VIDEO_LIMIT_BYTES:
            st.error(f"File is too large! Max allowed size is {LARGE_VIDEO_LIMIT_MB}MB. Please upload a smaller version of it.")
        
        else:
            # bytes object which can be translated to audio or video
            video_bytes = uploaded_file.read()
            
            # Try to get important frames from this video, if not don't add this key for further inference processing
            with st.spinner("Getting most important moments from your video."):

                try:
                    important_frames = read_important_frames(video_bytes, 100)

                    st.success(f"Got important moments.")

                    # add important frames to session state and redirect to model inference page
                    sst["important_frames"] = important_frames
                    
                except Exception as e:
                    st.write(f"Sorry couldn't extract important frames from this video & can't rate this on movie scale, because of error: {e}")


            # Try to get audio from this video, if not don't add this key for further inference processing
            with st.spinner("Getting audio transcript from your video for summary"):
                try:
                    audio_transcript_bytes = extract_audio(video_bytes)       
                
                    st.success(f"Got audio transcript.")

                    # add audio transcript to session state
                    sst["audio_transcript"] = audio_transcript_bytes
                
                except Exception as e:
                    st.write(f"Sorry couldn't extract audio from this video & can't rate summarize it, because of error: {e}")


            st.button("Summarize & Analyze Video",
                      on_click = navigate_to,
                      args = ("model_inference_page",)
                      )



async def model_inference_page():
    
    # check if frames are present and they are non-empty
    if "important_frames" in sst:

        important_frames = sst["important_frames"]
        with st.spinner("Generating Movie Scale rating for your video"):
            try:
                video_rating_scale = rate_video_frames(important_frames)
            except Exception as e:
                video_rating_scale = f"Sorry, we couldn't generate rating of your video because of this error: {e} "
        
        st.toast("Done")
        st.header("Movie Scale Rating of Your Video: ", divider = True)
        st.write(video_rating_scale)
        st.markdown("************************")
        

    # check if audio is present and it's non-empty
    if "audio_transcript" in sst:
        
        with st.spinner("Extracting text from audio file"):
            try:
                video_summary_text = get_text_from_audio(sst["audio_transcript"])
            except Exception as e:
                video_summary_text = f"Sorry, we couldn't extract text from audio of this file because of this error: {e} "
        st.toast("Done")

        if video_summary_text[:5] != "Sorry":
            with st.spinner("Summarizing text from entire transcript"):
                try:
                    video_summary_text = summarize_from_text(video_summary_text)
                except Exception as e:
                    video_summary_text = f"Sorry, we couldn't summarize text from audio of this file because of this error: {e} "
            st.toast("Done")

        st.header("Audio Transcript summary of your video: ", divider = True)
        st.write(video_summary_text)

    
    st.button("Go Home",
                      on_click = navigate_to,
                      args = ("landing_page",)
                      )