Upload app.py

app.py

@@ -0,0 +1,175 @@
+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()