Create app.py

app.py

@@ -0,0 +1,93 @@
+
+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=1)
+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):
+
+    # Define a regular expression pattern to match YouTube video URLs
+    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})'
+    )
+
+    # Use re.search to find the video ID in the URL
+    match = re.search(pattern, video_link)
+
+    # If a match is found, return the video ID; otherwise, return None
+    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)
+