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app3.py

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+##STREAMLINK CODE
+import cv2
+import streamlink
+import streamlit as st
+import time
+import tempfile
+import base64
+import os
+from dotenv import load_dotenv
+from openai import OpenAI
+import assemblyai as aai
+
+# 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 extract_recent_frames(video_url, output_folder, duration=10, frames_per_second=1):
+    streams = streamlink.streams(video_url)
+
+    if not streams:
+        st.error("Error: Unable to retrieve streams. Make sure the YouTube video URL is valid.")
+        return
+
+    stream_url = streams['best'].url
+
+    cap = cv2.VideoCapture(stream_url)
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    total_frames = int(fps * duration)
+    frame_interval = int(fps / frames_per_second)
+
+    frame_count = 0
+    start_time = time.time()
+
+    extracted_frames = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            st.error("Error: Couldn't read frame.")
+            break
+
+        elapsed_time = time.time() - start_time
+        if frame_count % frame_interval == 0 and elapsed_time <= duration:
+            # Convert frame to base64
+            _, buffer = cv2.imencode(".jpg", frame)
+            base64_frame = base64.b64encode(buffer).decode("utf-8")
+            extracted_frames.append(base64_frame)
+
+        frame_count += 1
+
+        if elapsed_time > duration:
+            break
+
+    cap.release()
+
+    return extracted_frames
+
+
+
+def main():
+    st.title("Insightly Live Video Analysis")
+
+    youtube_video_url = st.text_input("Enter YouTube Video URL:")
+    duration = st.slider("Select Duration (seconds):", min_value=1, max_value=60, value=10)
+    frames_per_second = st.slider("Select Frames per Second:", min_value=1, max_value=10, value=1)
+
+    if st.button("Extract Frames"):
+        st.info("Extracting frames. Please wait...")
+        extracted_frames = extract_recent_frames(youtube_video_url, "temp_frames", duration, frames_per_second)
+
+        if extracted_frames:
+            st.success("Frames extracted successfully!")
+            
+            # Display frames in a grid format with frame description on click
+            display_frame_grid(extracted_frames)
+
+
+
+        else:
+            st.error("Failed to extract frames.")
+
+#####################33
+def generate_description(base64_frames):
+    try:
+        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
+    except Exception as e:
+        print(f"Error in generate_description: {e}")
+        return None
+
+#########################################3333
+
+def display_frame_grid(extracted_frames):
+    cols_per_row = 3
+    n_frames = len(extracted_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]:
+                    # Decode base64 and display the frame
+                    decoded_frame = base64.b64decode(extracted_frames[frame_idx])
+                    st.image(decoded_frame, channels="BGR", caption=f'Frame {frame_idx + 1}', use_column_width=True, output_format="JPEG")
+
+
+
+
+if __name__ == "__main__":
+    main()