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app.py

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+import gradio as gr
+import pandas as pd
+from googleapiclient.discovery import build
+import re
+from transformers import pipeline
+
+# ⛔ Replace with your actual API key
+YOUTUBE_API_KEY = "AIzaSyBHwvf0dEMzAkrHol7FBoWu_1cnwGDMAvA"
+
+# Setup: Load Hugging Face sentiment analysis pipeline
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+# Function to extract video ID from YouTube URL
+def extract_video_id(url):
+    patterns = [
+        r"(?:youtube\.com\/watch\?v=|youtu\.be\/|youtube\.com\/embed\/)([^&\n?#]+)",
+        r"youtube\.com\/shorts\/([^&\n?#]+)"
+    ]
+    for pattern in patterns:
+        match = re.search(pattern, url)
+        if match:
+            return match.group(1)
+    return None
+
+# Function to fetch comments using YouTube API
+def fetch_comments(video_url, max_results=10):
+    video_id = extract_video_id(video_url)
+    if not video_id:
+        return pd.DataFrame({"error": ["Invalid YouTube URL"]})
+    
+    youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
+    request = youtube.commentThreads().list(
+        part="snippet",
+        videoId=video_id,
+        maxResults=max_results,
+        textFormat="plainText"
+    )
+    comments = []
+    try:
+        response = request.execute()
+        for item in response["items"]:
+            comment = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"]
+            comments.append(comment)
+        return pd.DataFrame({"Comment": comments})
+    except Exception as e:
+        return pd.DataFrame({"error": [str(e)]})
+
+# Main analysis function
+def analyze_video(video_url, max_comments=10):
+    df = fetch_comments(video_url, max_comments)
+    if "error" in df.columns:
+        return df.to_string(index=False)
+    
+    results = []
+    for comment in df["Comment"]:
+        result = sentiment_pipeline(comment[:512])[0]
+        results.append({
+            "Comment": comment,
+            "Sentiment": result["label"],
+            "Score": round(result["score"], 3)
+        })
+    result_df = pd.DataFrame(results)
+    return result_df
+
+# Gradio UI
+with gr.Blocks(title="YouTube Comment Sentiment Analyzer") as demo:
+    gr.Markdown("# 📊 YouTube Comment Sentiment Analyzer")
+
+    video_url = gr.Textbox(label="📺 YouTube Video URL", placeholder="Paste the video link here")
+    max_comments = gr.Slider(1, 100, value=10, step=1, label="Number of Comments")
+
+    btn = gr.Button("Analyze")
+
+    output = gr.Dataframe(label="Sentiment Analysis Result", interactive=False)
+
+    btn.click(fn=analyze_video, inputs=[video_url, max_comments], outputs=output)
+
+demo.launch()

requirements.txt

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+gradio==4.10.0
+google-api-python-client==2.115.0
+transformers==4.38.2
+torch
+pandas