Create app.py

app.py

@@ -0,0 +1,86 @@
+import streamlit as st
+from dotenv import load_dotenv
+import googleapiclient.errors
+import googleapiclient.discovery
+import os
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    'nlptown/bert-base-multilingual-uncased-sentiment')
+model = AutoModelForSequenceClassification.from_pretrained(
+    'nlptown/bert-base-multilingual-uncased-sentiment')
+
+load_dotenv()
+api_key = os.getenv("API_KEY")
+
+
+def get_comments(youtube, **kwargs):
+    comments = []
+    results = youtube.commentThreads().list(**kwargs).execute()
+
+    while results:
+        for item in results['items']:
+            comment = item['snippet']['topLevelComment']['snippet']['textDisplay']
+            comments.append(comment)
+
+        # check if there are more comments
+        if 'nextPageToken' in results:
+            kwargs['pageToken'] = results['nextPageToken']
+            results = youtube.commentThreads().list(**kwargs).execute()
+        else:
+            break
+
+    return comments
+
+
+def get_video_comments(video_id, api_key):
+    os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"
+
+    youtube = googleapiclient.discovery.build(
+        "youtube", "v3", developerKey=api_key)
+
+    comments = get_comments(youtube, part="snippet",
+                            videoId=video_id, textFormat="plainText")
+    return comments
+
+
+def makevideoid(url):
+    if "?v=" in url:
+        video_id = url.split("=")[1].split("&")[0]
+        return video_id
+    else:
+        video_id = url.split("/")[3].split("?")[0]
+        return video_id
+
+
+st.title("YouTube Comment Sentiment Analysis")
+
+# User input for video URL
+video_url = st.text_input("Enter YouTube Video URL")
+
+
+# Create a radio button
+
+
+# Content to be displayed based on the radio button state
+
+
+if video_url:
+
+    videoid = makevideoid(video_url)
+    comments = get_video_comments(videoid, api_key)
+
+    # Display comments and sentiment analysis
+    num = 0
+    with st.expander("Comment with sentiment analysis"):
+        for i, comment in enumerate(comments, 1):
+            tokens = tokenizer.encode(
+                comment, return_tensors='pt', max_length=512)
+            result = model(tokens)
+            sentiment = int(torch.argmax(result.logits)) + 1
+            num += sentiment
+
+            st.write(f"Comment {i}: {comment} (Sentiment: {sentiment})")
+    st.title(num/len(comments))