Create app.py

app.py

@@ -0,0 +1,148 @@
+import os
+import re
+from googleapiclient.discovery import build
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+import openai
+import gradio as gr
+import time
+
+# Set up the YouTube Data API key
+YOUTUBE_API_KEY = 'AIzaSyB18WjBnkOYmTO3xiWlRqcVgwMAlPtLE0w'
+
+def extract_video_id(url):
+    if "youtu.be" in url:  # Mobile YouTube URL
+        return get_redirected_url_video_id(url)
+    else:  # Desktop YouTube URL
+        video_id = re.search(r"v=([^&]+)", url).group(1)
+        return video_id
+
+def get_redirected_url_video_id(mobile_url):
+    # Set up Chrome options to run headless (without opening a visible browser window)
+    chrome_options = Options()
+    chrome_options.add_argument("--headless")
+    chrome_options.add_argument("--disable-gpu")
+
+    # Specify the path to the ChromeDriver executable
+    chrome_driver_path = '/path/to/chromedriver'  # Update this path
+
+    # Create a new Chrome session
+    service = Service(chrome_driver_path)
+    driver = webdriver.Chrome(service=service, options=chrome_options)
+
+    # Open the mobile YouTube link
+    driver.get(mobile_url)
+
+    # Wait for a few seconds to ensure the page has loaded
+    time.sleep(1)
+
+    # Get the current URL (which should be the full YouTube URL)
+    current_url = driver.current_url
+
+    # Close the browser
+    driver.quit()
+
+    # Extract the video ID from the current URL
+    video_id = re.search(r"v=([^&]+)", current_url).group(1)
+    return video_id
+
+
+def get_youtube_comments(video_id):
+    youtube = build('youtube', 'v3', developerKey=YOUTUBE_API_KEY)
+    comments = []
+    try:
+        # Request to get comments
+        request = youtube.commentThreads().list(
+            part="snippet",
+            videoId=video_id,
+            maxResults=200
+        )
+        response = request.execute()
+        
+        # Extract comments
+        for item in response['items']:
+            comment_data = item['snippet']['topLevelComment']['snippet']
+            comments.append({
+                'text': comment_data['textOriginal'],
+                'like_count': comment_data['likeCount']
+            })
+        
+        while 'nextPageToken' in response and len(comments) < 1000:
+            request = youtube.commentThreads().list(
+                part="snippet",
+                videoId=video_id,
+                pageToken=response['nextPageToken'],
+                maxResults=100
+            )
+            response = request.execute()
+            for item in response['items']:
+                comment_data = item['snippet']['topLevelComment']['snippet']
+                comments.append({
+                    'text': comment_data['textOriginal'],
+                    'like_count': comment_data['likeCount']
+                })
+                if len(comments) >= 1000:
+                    break
+    except Exception as e:
+        print(f"An error occurred: {e}")
+    
+    # Sort comments by like count in descending order and take the top 20
+    comments = sorted(comments, key=lambda x: x['like_count'], reverse=True)[:20]
+    return [comment['text'] for comment in comments]
+
+
+def generate_story(comments, temperature=0.7):
+    words = []
+    word_count = 0
+
+    for comment in comments:
+        comment_words = comment.split()
+        if word_count + len(comment_words) > 1000:
+            break
+        words.extend(comment_words)
+        word_count += len(comment_words)
+
+    comments_text = " ".join(words)
+    client = openai
+    completion = client.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "system", "content": f"""
+                    
+                        Read all the comments, understand the emotions people are feeling and pick any random emotion 
+                        and create a story in first person (the person can be randomly young or old and the story can be
+                        based in past or future) based on that emotion picking a random character keep the words 
+                        simple and a bit profound but overall simple words. Give more weight to the comments that 
+                        come earlier in sequence. The comments are given here: {comments_text}"""},
+                  {"role": "user", "content": """
+                        ignore the comment which has lyrics of the song, 
+                  ignore all comments similar to 'anyone in 2024', Keep the story randomly between 50-120 words. 
+                  don't mention the emotion chosen just start the story. 
+                  sometimes include bits where you tell how this song makes you feel. be very nostalgic about a feeling or a place this
+                  takes you to. half the times end the story with a hopeful future or a dark end or humorous.. choose randomly"""}]
+    ,temperature=temperature)
+    return completion.choices[0].message.content
+
+
+# Main function to execute the process
+def main(youtube_url, temperature):
+    video_id = extract_video_id(youtube_url)
+    comments = get_youtube_comments(video_id) 
+    story = generate_story(comments, temperature)
+    return story
+
+
+# Create Gradio interface
+youtube_url_input = gr.Textbox(label="YouTube URL")
+temperature_input = gr.Slider(minimum=0.0, maximum=2.0, value=0.7, label="Temperature (creativity)")
+
+iface = gr.Interface(
+    fn=main, 
+    inputs=[youtube_url_input, temperature_input], 
+    outputs="text", 
+    title="Let's hear a Story",
+    description="Enter a YouTube video URL to read a story which will capture the emotions of thousands of people before you who have listened to this and left comments :). The stories are AI-generated but does that mean it has never happened before or never will? Maybe the reader finds their own story with AI"
+)
+
+# Launch the interface
+iface.launch()