|
|
| import os |
|
|
| os.system("python3 -m pip install --upgrade pip") |
| os.system("pip install httpx==0.24.1") |
| os.system("pip uninstall -y gradio") |
| os.system("pip install gradio==3.1.4") |
|
|
| import gradio as gr |
| import hopsworks |
| import joblib |
| import pandas as pd |
| from googleapiclient.discovery import build |
| import re |
|
|
| hopsworks_key = "LR2zRcmisfNRQu0h.Hk1RWXOxv3HzMk54dE7iYDFMawiK6PYxb42sjHx8iQsc7D0h6Fsy76Ult5OJFmSi" |
|
|
| youtube = build( |
| 'youtube', |
| 'v3', |
| developerKey="AIzaSyAOsM68BSlRzcCReBf1Houhoe9zvTAaNFU" |
| ) |
|
|
| project = hopsworks.login(api_key_value=hopsworks_key) |
| fs = project.get_feature_store() |
|
|
|
|
| mr = project.get_model_registry() |
| model = mr.get_model("comments_model", version=2) |
| model_dir = model.download() |
| model = joblib.load(model_dir + "/comments_model.pkl") |
| vectorizer = joblib.load(model_dir + "/vectorizer.pkl") |
| print("Model downloaded") |
|
|
| def get_video_id(video_link): |
|
|
| |
| pattern = ( |
| r'(?:https?://)?(?:www\.)?' |
| '(?:youtube\.com/.*?[?&]v=|youtu\.be/|youtube\.com/embed/|youtube\.com/v/|youtube\.com/e/|youtube\.com/user/[^/]+/u/0/|www\.youtube\.com/user/[^/]+/u/0/|youtube\.com/s[^/]+/|www\.youtube\.com/s[^/]+/|youtube\.com/channel/|youtube\.com/c/|youtube\.com/user/[^/]+/|youtube\.com/user/[^/]+/live/|twitch\.tv/)' |
| '([^"&?/ ]{11})' |
| ) |
|
|
| |
| match = re.search(pattern, video_link) |
|
|
| |
| return match.group(1) if match else None |
|
|
|
|
| def sentiment(video_link): |
| print("Calling function") |
| video_id = get_video_id(video_link) |
| request = youtube.commentThreads().list( |
| part="snippet", |
| videoId=video_id, |
| maxResults=100 |
| ) |
| response = request.execute() |
|
|
| comments = [] |
| for item in response['items']: |
| comment = item['snippet']['topLevelComment']['snippet'] |
| comment_text = ''.join(e for e in comment['textDisplay'] if (e.isalnum() or e.isspace())) |
| comments.append([comment_text]) |
|
|
| df = pd.DataFrame(comments, columns=['comment']) |
| df = df.dropna(subset=['comment']) |
| comments_features = vectorizer.transform(df['comment']) |
| predictions = model.predict(comments_features) |
| positive_count = sum(predictions > 0) |
| negative_count = sum(predictions < 0) |
| total_count = len(predictions) |
| positive_percentage = (positive_count / total_count) * 100 |
| negative_percentage = (negative_count / total_count) * 100 |
| return positive_count, negative_count, f"{positive_percentage:.2f}%", f"{negative_percentage:.2f}%" |
| |
| demo = gr.Interface( |
| fn=sentiment, |
| title="YouTube comment sentiment analysis", |
| description="Experiment with YouTube comments to predict the YouTube video sentiments.", |
| allow_flagging="never", |
| inputs=gr.Textbox(type="text", label="input YouTube video link",variable="video_link"), |
| outputs=[ |
| gr.Number(label="The number of positive comments", default=0), |
| gr.Number(label="The number of negative comments", default=0), |
| gr.Textbox(label="Percentage of positive comments", name="positive_percentage"), |
| gr.Textbox(label="Percentage of negative comments", name="negative_percentage"), |
| ], |
| ) |
|
|
| demo.launch(debug=True, share=True) |
|
|
|
|
