Delete backup-app1-app.py

backup-app1-app.py

@@ -1,129 +0,0 @@
-import streamlit as st
-from openai import OpenAI
-import os
-import base64
-import cv2
-from moviepy.editor import VideoFileClip
-
-API_KEY = os.getenv('gpt4okey')
-
-# Set the API key and model name
-MODEL = "gpt-4o"
-
-# Switch to project based with limits and use org id and key to identify run pool
-# models for GPT-4o project:    gpt-4o-2024-05-13   (gpt-4o)
-#client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "<your OpenAI API key if not set as an env var>"))
-client = OpenAI(api_key=API_KEY)
-
-
-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. Help me with my math homework!"},
-                {"role": "user", "content": [
-                    {"type": "text", "text": "What's the area of the triangle?"},
-                    {"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_video(video_input):
-    if video_input:
-        base64Frames, audio_path = process_video_frames(video_input)
-        transcription = client.audio.transcriptions.create(
-            model="whisper-1",
-            file=open(audio_path, "rb"),
-        )
-        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: {transcription.text}"}
-                ]},
-            ],
-            temperature=0,
-        )
-        st.markdown(response.choices[0].message.content)
-
-def process_video_frames(video_path, seconds_per_frame=2):
-    base64Frames = []
-    base_video_path, _ = os.path.splitext(video_path.name)
-    video = cv2.VideoCapture(video_path.name)
-    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
-    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()
-    audio_path = f"{base_video_path}.mp3"
-    clip = VideoFileClip(video_path.name)
-    clip.audio.write_audiofile(audio_path, bitrate="32k")
-    clip.audio.close()
-    clip.close()
-    return base64Frames, audio_path
-
-def main():
-    st.title("Omni Demo")
-    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_video(video_input)
-
-if __name__ == "__main__":
-    main()