Delete backup-app3-0521-app.py

backup-app3-0521-app.py

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-import streamlit as st
-import openai
-from openai import OpenAI
-import os
-import base64
-import cv2
-from moviepy.editor import VideoFileClip
-
-# documentation
-# 1. Cookbook:  https://cookbook.openai.com/examples/gpt4o/introduction_to_gpt4o
-# 2. Configure your Project and Orgs to limit/allow Models:  https://platform.openai.com/settings/organization/general
-# 3. Watch your Billing!  https://platform.openai.com/settings/organization/billing/overview
-
-
-# Set API key and organization ID from environment variables
-openai.api_key = os.getenv('OPENAI_API_KEY')
-openai.organization = os.getenv('OPENAI_ORG_ID')
-client = OpenAI(api_key= os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID'))
-
-# Define the model to be used
-#MODEL = "gpt-4o"
-MODEL = "gpt-4o-2024-05-13"
-
-def process_text():
-    text_input = st.text_input("Enter your text:")
-    if text_input:
-        completion = client.chat.completions.create(
-            model=MODEL,
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant. Help me with my math homework!"},
-                {"role": "user", "content": f"Hello! Could you solve {text_input}?"}
-            ]
-        )
-        st.write("Assistant: " + completion.choices[0].message.content)
-
-def process_image(image_input):
-    if image_input:
-        base64_image = base64.b64encode(image_input.read()).decode("utf-8")
-        response = client.chat.completions.create(
-            model=MODEL,
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
-                {"role": "user", "content": [
-                    {"type": "text", "text": "Help me understand what is in this picture and list ten facts as markdown outline with appropriate emojis that describes what you see."},
-                    {"type": "image_url", "image_url": {
-                        "url": f"data:image/png;base64,{base64_image}"}
-                    }
-                ]}
-            ],
-            temperature=0.0,
-        )
-        st.markdown(response.choices[0].message.content)
-
-def process_audio(audio_input):
-    if audio_input:
-        transcription = client.audio.transcriptions.create(
-            model="whisper-1",
-            file=audio_input,
-        )
-        response = client.chat.completions.create(
-            model=MODEL,
-            messages=[
-            {"role": "system", "content":"""You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown."""},
-            {"role": "user", "content": [{"type": "text", "text": f"The audio transcription is: {transcription.text}"}],}
-            ],
-            temperature=0,
-        )
-        st.markdown(response.choices[0].message.content)
-
-def process_audio_for_video(video_input):
-    if video_input:
-        transcription = client.audio.transcriptions.create(
-            model="whisper-1",
-            file=video_input,
-        )
-        response = client.chat.completions.create(
-            model=MODEL,
-            messages=[
-            {"role": "system", "content":"""You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown."""},
-            {"role": "user", "content": [{"type": "text", "text": f"The audio transcription is: {transcription}"}],}
-            ],
-            temperature=0,
-        )
-        st.markdown(response.choices[0].message.content)
-        return response.choices[0].message.content
-
-def save_video(video_file):
-    # Save the uploaded video file
-    with open(video_file.name, "wb") as f:
-        f.write(video_file.getbuffer())
-    return video_file.name
-
-def process_video(video_path, seconds_per_frame=2):
-    base64Frames = []
-    base_video_path, _ = os.path.splitext(video_path)
-    video = cv2.VideoCapture(video_path)
-    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = video.get(cv2.CAP_PROP_FPS)
-    frames_to_skip = int(fps * seconds_per_frame)
-    curr_frame = 0
-
-    # Loop through the video and extract frames at specified sampling rate
-    while curr_frame < total_frames - 1:
-        video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
-        success, frame = video.read()
-        if not success:
-            break
-        _, buffer = cv2.imencode(".jpg", frame)
-        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
-        curr_frame += frames_to_skip
-
-    video.release()
-
-    # Extract audio from video
-    audio_path = f"{base_video_path}.mp3"
-    clip = VideoFileClip(video_path)
-    clip.audio.write_audiofile(audio_path, bitrate="32k")
-    clip.audio.close()
-    clip.close()
-
-    print(f"Extracted {len(base64Frames)} frames")
-    print(f"Extracted audio to {audio_path}")
-
-    return base64Frames, audio_path
-
-def process_audio_and_video(video_input):
-    if video_input is not None:
-        # Save the uploaded video file
-        video_path = save_video(video_input )
-    
-        # Process the saved video
-        base64Frames, audio_path = process_video(video_path, seconds_per_frame=1)
-
-        # Get the transcript for the video model call
-        transcript = process_audio_for_video(video_input)
-        
-        # Generate a summary with visual and audio
-        response = client.chat.completions.create(
-            model=MODEL,
-            messages=[
-                {"role": "system", "content": """You are generating a video summary. Create a summary of the provided video and its transcript. Respond in Markdown"""},
-                {"role": "user", "content": [
-                    "These are the frames from the video.",
-                    *map(lambda x: {"type": "image_url",
-                                    "image_url": {"url": f'data:image/jpg;base64,{x}', "detail": "low"}}, base64Frames),
-                    {"type": "text", "text": f"The audio transcription is: {transcript}"}
-                ]},
-            ],
-            temperature=0,
-        )
-    
-        st.markdown(response.choices[0].message.content)
-
-
-def main():
-    st.markdown("### OpenAI GPT-4o Model")
-    st.markdown("#### The Omni Model with Text, Audio, Image, and Video")
-    option = st.selectbox("Select an option", ("Text", "Image", "Audio", "Video"))
-    if option == "Text":
-        process_text()
-    elif option == "Image":
-        image_input = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-        process_image(image_input)
-    elif option == "Audio":
-        audio_input = st.file_uploader("Upload an audio file", type=["mp3", "wav"])
-        process_audio(audio_input)
-    elif option == "Video":
-        video_input = st.file_uploader("Upload a video file", type=["mp4"])
-        process_audio_and_video(video_input)
-
-if __name__ == "__main__":
-    main()