Delete app.py

app.py

@@ -1,174 +0,0 @@
-import cv2
-import streamlit as st
-import tempfile
-import base64
-import os
-from dotenv import load_dotenv
-from openai import OpenAI
-import assemblyai as aai
-from moviepy.editor import *
-
-
-
-# Load environment variables
-load_dotenv()
-aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
-OpenAI.api_key = os.getenv("OPENAI_API_KEY")
-client = OpenAI()
- 
-def main():
-    st.title('Insightly Video Content Moderation')
- 
-    # Video upload section
-    uploaded_video = st.file_uploader('Upload a video', type=["mp4", "avi", "mov"])
- 
-    if uploaded_video is not None:
-        # Save the video to a temp file
-        tfile = tempfile.NamedTemporaryFile(delete=False)
-        tfile.write(uploaded_video.read())
-        video_file_path = tfile.name
-        tfile.close()
-
-        transcriber = aai.Transcriber()
-        transcript = transcriber.transcribe(tfile.name)
-
-        # Process the video and display frames in a grid layout
-        base64_frames = video_to_base64_frames(video_file_path)
-        display_frame_grid(base64_frames[::30])  # Display every 30th frame in a 3-column grid
- 
-    st.write("Actions:")  # Header for the actions/buttons section
-
-    # Creating four columns to align the buttons
-    col1, col2, col3, col4 = st.columns(4)
-
-    with col1:
-        if st.button("Description"):
-            st.session_state['description'] = generate_description(base64_frames) if 'description' not in st.session_state else st.session_state['description']
-    
-    with col2:
-        if st.button("Frame Description"):
-            st.session_state['frame_description'] = generate_frame_description(base64_frames) if 'frame_description' not in st.session_state else st.session_state['frame_description']
-
-    with col3:
-        if st.button("Generate Transcript"):
-            st.session_state['transcript'] = transcript.text if 'transcript' not in st.session_state else st.session_state['transcript']
-    
-    with col4:
-        if st.button("Category of Video"):
-            st.session_state['category'] = generate_category(base64_frames) if 'category' not in st.session_state else st.session_state['category']
-
-    # If any value exists in session state then display it
-    if 'description' in st.session_state and st.session_state['description']:
-        st.subheader("Video Description")
-        st.write(st.session_state['description'])
-    
-    if 'frame_description' in st.session_state and st.session_state['frame_description']:
-        st.subheader("Frame Description")
-        st.write(st.session_state['frame_description'])
-    
-    if 'transcript' in st.session_state and st.session_state['transcript']:
-        st.subheader("Video Transcript")
-        st.write(st.session_state['transcript'])
-    
-    if 'category' in st.session_state and st.session_state['category']:
-        st.subheader("Video Category")
-        st.write(st.session_state['category'])
-
-
-
-
-            
-
-def video_to_base64_frames(video_file_path):
-    # Logic to extract all frames from the video and convert them to base64
-    video = cv2.VideoCapture(video_file_path)
-    base64_frames = []
- 
-    while video.isOpened():
-        success, frame = video.read()
-        if not success:
-            break
- 
-        _, buffer = cv2.imencode('.jpg', frame)
-        base64_frame = base64.b64encode(buffer).decode('utf-8')
-        base64_frames.append(base64_frame)
- 
-    video.release()
-    return base64_frames
-
-#########################################
-#Generate Video description
-def generate_description(base64_frames):
-    prompt_messages = [
-        {
-            "role": "user",
-            "content": [
-                "1. Generate a description for this sequence of video frames in about 90 words.\
-                Return the following : 1. List of objects in the video 2. Any restrictive content or sensitive content and if so which frame ",
-                *map(lambda x: {"image": x, "resize": 428}, base64_frames[0::30]),
-            ],
-        },
-    ]
-    response = client.chat.completions.create(
-        model="gpt-4-vision-preview",
-        messages=prompt_messages,
-        max_tokens=3000,
-    )
-    return response.choices[0].message.content
-
-#Generate frame description
-def generate_frame_description(base64_frames):
-    prompt_messages = [
-        {
-            "role": "user",
-            "content": [
-                "Describe what is happening in each frame.",
-                *map(lambda x: {"image": x, "resize": 428}, base64_frames[0::30]),
-            ],
-        },
-    ]
-    response = client.chat.completions.create(
-        model="gpt-4-vision-preview",
-        messages=prompt_messages,
-        max_tokens=3000,
-    )
-    return response.choices[0].message.content
-
-
-
-#Generate Category of Video
-def generate_category(base64_frames):
-    prompt_messages = [
-        {
-            "role": "user",
-            "content": [
-                "What category can this video be tagged to?",
-                *map(lambda x: {"image": x, "resize": 428}, base64_frames[0::30]),
-            ],
-        },
-    ]
-    response = client.chat.completions.create(
-        model="gpt-4-vision-preview",
-        messages=prompt_messages,
-        max_tokens=3000,
-    )
-    return response.choices[0].message.content
-
-
-
-
-######################## 
-def display_frame_grid(base64_frames):
-    cols_per_row = 3
-    n_frames = len(base64_frames)
-    for idx in range(0, n_frames, cols_per_row):
-        cols = st.columns(cols_per_row)
-        for col_index in range(cols_per_row):
-            frame_idx = idx + col_index
-            if frame_idx < n_frames:
-                with cols[col_index]:
-                    frame = base64_frames[frame_idx]
-                    st.image(base64.b64decode(frame), caption=f'Frame {frame_idx * 30 + 1}', width=200)
- 
-if __name__ == '__main__':
-    main()