Add application file

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import pandas as pd
+import numpy as np
+import googleapiclient.discovery
+
+# Initialize the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+
+# Function to get comments from a YouTube video
+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
+
+# Function to get sentiment score
+def sentiment_score(comment):
+    sentiment = model(tokenizer.encode(comment, return_tensors='pt'))
+    return torch.argmax(sentiment.logits).item() + 1
+
+# Gradio function to analyze video comments
+def analyze_video(video_id, api_key):
+    youtube = googleapiclient.discovery.build("youtube", "v3", developerKey=api_key)
+    comments = get_comments(youtube, part="snippet", videoId=video_id, textFormat="plainText")
+    
+    if not comments:
+        return "No comments found."
+
+    df = pd.DataFrame(np.array(comments[:100]), columns=['comments'])
+    df['sentiment'] = df['comments'].apply(lambda x: sentiment_score(x[:512]))
+
+    sentiment_counts = df['sentiment'].value_counts().sort_index().to_dict()
+
+    # Returning as a dictionary, where the key is the sentiment score (1-5) and value is the count
+    return sentiment_counts
+
+# Creating the Gradio Interface
+gr_interface = gr.Interface(
+    fn=analyze_video,
+    inputs=[gr.Textbox(label="YouTube Video ID"), gr.Textbox(label="YouTube API Key")],
+    outputs="json",
+    title="YouTube Comment Sentiment Analysis",
+    description="Input a YouTube video ID and your API key to analyze the sentiment of the comments."
+)
+
+# Launch the Gradio app
+gr_interface.launch()

requirements.txt

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+google-api-python-client