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import os
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os.environ["USE_TF"] = "0"
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import os
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import re
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import logging
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import textwrap
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import matplotlib.pyplot as plt
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import plotly.express as px
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import pandas as pd
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from googleapiclient.discovery import build
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from transformers import pipeline
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from dotenv import load_dotenv
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from transformers import pipeline
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import streamlit as st
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load_dotenv()
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API_KEY = os.getenv("YOUTUBE_API_KEY")
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@st.cache_resource
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def load_sentiment_model():
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from transformers import pipeline
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return pipeline(model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", top_k=None)
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sentiment_classifier = load_sentiment_model()
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def extract_video_id(url):
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patterns = [
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r"(?:https?:\/\/)?(?:www\.)?youtube\.com\/watch\?v=([^&]+)",
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r"(?:https?:\/\/)?(?:www\.)?youtube\.com\/embed\/([^?]+)",
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r"(?:https?:\/\/)?(?:www\.)?youtube\.com\/v\/([^?]+)",
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r"(?:https?:\/\/)?youtu\.be\/([^?]+)"
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]
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for pattern in patterns:
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match = re.search(pattern, url)
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if match:
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return match.group(1)
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return None
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def get_video_title(video_id):
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youtube = build("youtube", "v3", developerKey=API_KEY)
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try:
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request = youtube.videos().list(part="snippet", id=video_id)
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response = request.execute()
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return response["items"][0]["snippet"]["title"]
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except:
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return "Unknown Title"
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def get_comments(video_id, max_results=100):
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youtube = build("youtube", "v3", developerKey=API_KEY)
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comments = []
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next_page_token = None
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try:
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while len(comments) < max_results:
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request = youtube.commentThreads().list(
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part="snippet",
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videoId=video_id,
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maxResults=min(100, max_results - len(comments)),
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textFormat="plainText",
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pageToken=next_page_token
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)
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response = request.execute()
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for item in response.get("items", []):
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comments.append(item["snippet"]["topLevelComment"]["snippet"]["textDisplay"])
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next_page_token = response.get("nextPageToken")
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if not next_page_token:
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break
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except Exception as e:
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return [], str(e)
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return comments, None
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def analyze_sentiment(comments):
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results = []
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counts = {"positive": 0, "neutral": 0, "negative": 0}
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all_sentiments = sentiment_classifier(comments, batch_size=8)
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for comment, scores in zip(comments, all_sentiments):
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sentiment = max(scores, key=lambda x: x["score"])
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label = sentiment["label"]
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results.append({"Comment": comment, "Sentiment": label, "Score": sentiment["score"]})
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counts[label] += 1
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return results, counts
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def plot_pie_chart(counts, video_title):
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labels = list(counts.keys())
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values = list(counts.values())
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fig = px.pie(
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names=labels,
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values=values,
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title=f"Sentiment Distribution",
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color=labels,
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color_discrete_map={
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'Positive': '#66bb6a',
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'Neutral': '#ffee58',
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'Negative': '#ef5350'
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},
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)
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fig.update_traces(textinfo='percent+label', textfont_size=14)
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fig.update_layout(
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margin=dict(l=20, r=20, t=40, b=20),
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height=350,
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width=350,
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showlegend=False,
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title_x=0
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)
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return fig
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def get_overall_sentiment(counts):
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return f"Overall Video Sentiment: {max(counts, key=counts.get).upper()}" |
