Create app.py

app.py

@@ -0,0 +1,83 @@
+import os
+import gradio as gr
+import youtube_dl
+import openai
+import requests
+from datetime import datetime, timedelta
+import pandas as pd
+import moviepy.editor
+import openai
+from IPython.display import Audio
+import youtube_dl
+import json
+
+
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+RAPIDAPI_KEY = os.environ.get("OPENAI_API_KEY")
+
+def process_video(videoid, prompt):
+
+
+    url = "https://tiktok-video-no-watermark2.p.rapidapi.com/"
+
+    querystring = {"url":videoid,"hd":"0"}
+
+    headers = {
+	  "X-RapidAPI-Key": RAPIDAPI_KEY,
+	  "X-RapidAPI-Host": "tiktok-video-no-watermark2.p.rapidapi.com"
+    }
+
+    response = requests.request("GET", url, headers=headers, params=querystring)
+    # Download video
+    wmplay_link = response.json().get('data').get('wmplay')
+    ydl_opts = {
+        'format': 'worstaudio/worst',
+        'outtmpl': 'temp_audio.%(ext)s',
+        'postprocessors': [{
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'mp3',
+            'preferredquality': '64',
+        }]
+    }
+
+    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        try:
+            ydl.download([wmplay_link])
+        except youtube_dl.utils.DownloadError as e:
+            return f"Error: {e}", ""
+
+    # Get the transcript
+    with open("temp_audio.mp3", "rb") as audio_file:
+        try:
+            transcript = openai.Audio.transcribe("whisper-1", audio_file)
+        except openai.error.InvalidRequestError as e:
+            return f"Error: {e.http_body}", ""
+
+    # Get keywords and summary
+    messages = [{"role": "system", "content": prompt}]
+    messages.append({"role": "user", "content": transcript["text"]})
+
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo", messages=messages)
+        summary = response.choices[0].message.content
+        return transcript["text"], summary
+    except openai.error.InvalidRequestError as e:
+        return f"Error: {e.http_body}", ""
+
+# Gradio interface
+iface = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.inputs.Textbox(lines=5, label="Video ID", default = "7083232374267596078"),
+        gr.inputs.Textbox(lines=5, label="Prompt", default="You are a creative content writer. Based on this video transcript, I want you to extract 3-5 relevant keywords from the transcript. Then, I want you to summarise the video in 1-2 sentences. Make sure to delimit the parts with a new line. Finally, turn this into an amazing LinkedIn post that is 550-600 characters long. Include all of the LinkedIn post best practices. Delimit each section with a new line delimiter. Return the output in English.")
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="🖨️ Transcript"),
+        gr.outputs.Textbox(label="🤫 Keywords and summary")
+    ],
+    title="Video Transcript Processing",
+    description="Extract keywords and summarize a video's transcript."
+)
+
+iface.launch(debug = False, auth=(os.environ.get(USERNAME), os.environ.get(PASSWORD)))