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