Create app.py

app.py

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+import gradio as gr
+import requests
+from googleapiclient.discovery import build
+from transformers import pipeline
+import re
+
+# Setup Sentiment Analysis
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+# YouTube API Key
+YOUTUBE_API_KEY = "AIzaSyBuNxsm0LnHF0OkbYgMSNHnwu8iVUVi5gc"
+
+def extract_video_id(url):
+    match = re.search(r"(?:v=|youtu\.be/)([a-zA-Z0-9_-]{11})", url)
+    return match.group(1) if match else None
+
+def get_youtube_comments(video_url, max_results=20):
+    video_id = extract_video_id(video_url)
+    if not video_id:
+        return None, "Invalid YouTube URL."
+
+    youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
+    request = youtube.commentThreads().list(
+        part="snippet",
+        videoId=video_id,
+        maxResults=max_results,
+        order="relevance",
+        textFormat="plainText"
+    )
+    response = request.execute()
+
+    comments_data = []
+    for item in response["items"]:
+        comment_text = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"]
+        like_count = item["snippet"]["topLevelComment"]["snippet"].get("likeCount", 0)
+        comments_data.append((comment_text, like_count))
+
+    return comments_data, None
+
+def analyze_comments(video_url):
+    comments_data, error = get_youtube_comments(video_url)
+    if error:
+        return error
+
+    table_md = "| Comment | Sentiment | Likes |\n|---|---|---|\n"
+    summary_data = {
+        "positive": 0,
+        "neutral": 0,
+        "negative": 0,
+        "max_likes": -1,
+        "top_comment": "",
+        "top_sentiment": "",
+        "most_positive": "",
+        "most_negative": ""
+    }
+
+    for comment, likes in comments_data:
+        result = sentiment_analyzer(comment)[0]
+        sentiment = result["label"]
+
+        if sentiment == "POSITIVE":
+            summary_data["positive"] += 1
+        elif sentiment == "NEGATIVE":
+            summary_data["negative"] += 1
+        else:
+            summary_data["neutral"] += 1
+
+        table_md += f"| {comment} | {sentiment} | {likes} |\n"
+
+        if likes > summary_data["max_likes"]:
+            summary_data["max_likes"] = likes
+            summary_data["top_comment"] = comment
+            summary_data["top_sentiment"] = sentiment
+
+        if sentiment == "POSITIVE":
+            summary_data["most_positive"] = comment
+        if sentiment == "NEGATIVE":
+            summary_data["most_negative"] = comment
+
+    summary = (
+        f"\n\n### Summary:\n"
+        f"- Most liked comment: \"{summary_data['top_comment']}\" ({summary_data['max_likes']} likes, {summary_data['top_sentiment']})\n"
+        f"- Most positive comment: \"{summary_data['most_positive']}\"\n"
+        f"- Most negative comment: \"{summary_data['most_negative']}\"\n"
+        f"- Sentiment Count: {summary_data['positive']} Positive, {summary_data['neutral']} Neutral, {summary_data['negative']} Negative\n"
+    )
+
+    return table_md + summary
+
+interface = gr.Interface(
+    fn=analyze_comments,
+    inputs=gr.Textbox(label="Enter the Youtube Link:"),
+    outputs=gr.Markdown(),
+    title="YouTube Comment Sentiment Analyzer",
+    description="Paste a YouTube video link to analyze top comments for sentiment (Positive, Negative, Neutral)."
+)
+
+interface.launch()