Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import re
+import pandas as pd
+import googleapiclient.discovery
+import plotly.express as px 
+
+# Load the BERT tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+
+
+# Set up the YouTube API service
+api_service_name = "youtube"
+api_version = "v3"
+DEVELOPER_KEY = "AIzaSyC4Vx8G6nm3Ow9xq7NluTuCCJ1d_5w4YPE"  # Replace with your actual API key
+
+youtube = googleapiclient.discovery.build(api_service_name, api_version, developerKey=DEVELOPER_KEY)
+
+# Function to fetch comments for a video ID
+def scrape_comments(video_id):
+    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']
+        comments.append([
+            comment['textDisplay']
+        ])
+
+    comments_df = pd.DataFrame(comments, columns=['comment'])
+
+    # df.head(10).
+
+    return comments_df
+
+
+# Function to extract video ID from YouTube URL
+def extract_video_id(video_url):
+    match = re.search(r'(?<=v=)[\w-]+', video_url)
+    if match:
+        return match.group(0)
+    else:
+        st.error("Invalid YouTube video URL")
+
+# Function to fetch YouTube comments for a video ID
+def fetch_comments(video_id):
+    # Example using youtube-comment-scraper-python library
+    comments = scrape_comments(video_id)
+    return comments
+
+# Function to analyze sentiment for a single comment
+def analyze_sentiment(comment):
+    tokens = tokenizer.encode(comment, return_tensors="pt", max_length=512, truncation=True)
+    # input_ids = tokens['input_ids']
+    # attention_mask = tokens['attention_mask']
+
+    # result = model(input_ids, attention_mask=attention_mask)
+    result = model(tokens)
+
+    sentiment_id = torch.argmax(result.logits) + 1
+    if(sentiment_id > 3):
+        sentiment_label = "Positive"
+    elif(sentiment_id < 3):
+        sentiment_label = "Negative"
+    else:
+        sentiment_label = "Neutral"
+
+    return sentiment_label
+
+
+def main():
+    st.title("YouTube Comments Sentiment Analysis")
+    st.write("Enter a YouTube video link below:")
+
+    video_url = st.text_input("YouTube Video URL:")
+    if st.button("Extract Comments and Analyze"):
+        video_id = extract_video_id(video_url)
+        if video_id:
+            comments_df = fetch_comments(video_id)
+            # Comments is a dataframe of just the comments text
+            # st.write("Top 100 Comments extracted\n", comments_df)
+            comments_df['sentiment'] = comments_df['comment'].apply(lambda x: analyze_sentiment(x[:512]))
+            sentiment_counts = comments_df['sentiment'].value_counts()
+            positive_count = comments_df['sentiment'].value_counts().get('Positive', 0)
+            negative_count = comments_df['sentiment'].value_counts().get('Negative', 0)
+            neutral_count = comments_df['sentiment'].value_counts().get('Neutral', 0)
+
+                # Create pie chart in col2 with custom colors
+            fig_pie = px.pie(values=[positive_count, negative_count, neutral_count],
+                            names=['Positive', 'Negative', 'Neutral'],
+                            title='Pie chart representations',
+                            color=sentiment_counts.index,  # Use sentiment categories as colors
+                            color_discrete_map={'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue'})
+            st.plotly_chart(fig_pie, use_container_width=True)
+
+            # Create bar chart below the pie chart with custom colors
+            fig_bar = px.bar(x=sentiment_counts.index, y=sentiment_counts.values,
+                            labels={'x': 'Sentiment', 'y': 'Count'},
+                            title='Bar plot representations',
+                            color=sentiment_counts.index,  # Use sentiment categories as colors
+                            color_discrete_map={'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue'})
+            st.plotly_chart(fig_bar)
+
+
+if __name__ == "__main__":
+    main()
+