import sys import os import re from pathlib import Path import gradio as gr import pandas as pd import torch import matplotlib.pyplot as plt from googleapiclient.discovery import build try: from data_preprocessing import Vocabulary, clean_text from model_bilstm_attention import BiLSTMAttention except ImportError: sys.path.append(str(Path(__file__).parent)) from src.data_preprocessing import Vocabulary, clean_text from src.model_bilstm_attention import BiLSTMAttention API_KEY = "AIzaSyAlKTUhY9t3yaJvk0E2goCuLEtcsTOFMBM" # Modeller artik ana dizinde (root) MODELS_DIR = Path(__file__).parent DEVICE = torch.device('cpu') # ==================== LOAD MODEL ==================== def load_trained_model(): try: # Dosyalar artik yan yana vocab_path = MODELS_DIR / "vocabulary.pkl" model_path = MODELS_DIR / "BiLSTM_Attention_best.pt" vocab = Vocabulary.load(vocab_path) checkpoint = torch.load(model_path, map_location=DEVICE) config = checkpoint['config'].copy() config.pop('bidirectional', None) model = BiLSTMAttention(**config) model.load_state_dict(checkpoint['model_state_dict']) model.to(DEVICE) model.eval() return model, vocab except Exception as e: print(f"Error loading model: {e}") return None, None MODEL, VOCAB = load_trained_model() def extract_video_id(url): patterns = [ r"(?:https?://)?(?:www\.)?youtube\.com/watch\?v=([^&]+)", r"(?:https?://)?(?:www\.)?youtube\.com/embed/([^?]+)", r"(?:https?://)?(?:www\.)?youtube\.com/v/([^?]+)", r"(?:https?://)?youtu\.be/([^?]+)" ] for pattern in patterns: match = re.search(pattern, url) if match: return match.group(1) return None def get_video_title(video_id): try: youtube = build("youtube", "v3", developerKey=API_KEY) request = youtube.videos().list(part="snippet", id=video_id) response = request.execute() if "items" in response and len(response["items"]) > 0: return response["items"][0]["snippet"]["title"] return "Unknown Video" except: return "Unknown Video" def get_comments(video_id, max_results=100): try: youtube = build("youtube", "v3", developerKey=API_KEY) comments = [] next_page_token = None while len(comments) < max_results: request = youtube.commentThreads().list( part="snippet", videoId=video_id, maxResults=min(100, max_results - len(comments)), textFormat="plainText", pageToken=next_page_token ) response = request.execute() for item in response.get("items", []): comment = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"] comments.append(comment) next_page_token = response.get("nextPageToken") if not next_page_token: break return comments[:max_results], None except Exception as e: return [], str(e) def predict_sentiment(text, model, vocab, max_length=128): if model is None or vocab is None: return "neutral", [0.33, 0.34, 0.33] cleaned = clean_text(text) if not cleaned: return "neutral", [0.33, 0.34, 0.33] encoded = vocab.encode(cleaned) encoded = encoded + [0] * (max_length - len(encoded)) if len(encoded) < max_length else encoded[:max_length] input_ids = torch.tensor([encoded], dtype=torch.long).to(DEVICE) length = torch.tensor([min(len(vocab.encode(cleaned)), max_length)], dtype=torch.long).to(DEVICE) with torch.no_grad(): logits = model(input_ids, length) probabilities = torch.softmax(logits, dim=1).cpu().numpy()[0] label_map = {0: 'negative', 1: 'neutral', 2: 'positive'} return label_map[probabilities.argmax()], probabilities.tolist() def analyze_comments(comments): results = [] sentiment_counts = {"positive": 0, "neutral": 0, "negative": 0} for comment in comments: sentiment, probs = predict_sentiment(comment, MODEL, VOCAB) sentiment_counts[sentiment] += 1 results.append({ "Comment": comment[:100] + "..." if len(comment) > 100 else comment, "Sentiment": sentiment.capitalize(), "Confidence": f"{max(probs):.2%}" }) return results, sentiment_counts def plot_pie_chart(sentiment_counts, video_title): fig, ax = plt.subplots(figsize=(10, 7)) colors = {'positive': '#4CAF50', 'neutral': '#FFC107', 'negative': '#F44336'} chart_colors = [colors[label] for label in sentiment_counts.keys()] wedges, texts, autotexts = ax.pie( sentiment_counts.values(), labels=[label.capitalize() for label in sentiment_counts.keys()], autopct='%1.1f%%', startangle=140, colors=chart_colors, textprops={'fontsize': 12, 'weight': 'bold'} ) for autotext in autotexts: autotext.set_color('white') ax.set_title(f"Sentiment Analysis\n{video_title[:60]}", fontsize=14, fontweight='bold', pad=20) return fig def get_overall_sentiment(sentiment_counts): total = sum(sentiment_counts.values()) if total == 0: return "No comments analyzed" dominant = max(sentiment_counts, key=sentiment_counts.get) return f"Overall Sentiment: {dominant.upper()} ({sentiment_counts[dominant]/total*100:.1f}%)" def youtube_sentiment_analysis(url, num_of_comments): if MODEL is None or VOCAB is None: return "Model loading failed. Check logs.", None, None video_id = extract_video_id(url) if not video_id: return "Invalid URL", None, None video_title = get_video_title(video_id) comments, error = get_comments(video_id, int(num_of_comments)) if error: return f"Error: {error}", None, None if not comments: return "No comments found", None, None results, sentiment_counts = analyze_comments(comments) chart = plot_pie_chart(sentiment_counts, video_title) summary = get_overall_sentiment(sentiment_counts) return summary, chart, pd.DataFrame(results).head(10) example_urls = [ ["https://www.youtube.com/watch?v=0e9WuB0Ua98"], ["https://www.youtube.com/watch?v=3JZ_D3ELwOQ"], ] with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.HTML("

YouTube Sentiment Analysis

") gr.HTML("

Custom Bi-LSTM + Attention Model | Umut ABALI

") with gr.Row(): with gr.Column(): youtube_url = gr.Textbox(label="YouTube Video URL") num_comments = gr.Slider(10, 500, step=10, value=100, label="Max Comments") submit_btn = gr.Button("Analyze", variant="primary") gr.Examples(example_urls, inputs=youtube_url) with gr.Column(): output_summary = gr.Textbox(label="Summary") output_chart = gr.Plot(label="Chart") output_table = gr.Dataframe(label="Top 10 Comments") submit_btn.click( youtube_sentiment_analysis, inputs=[youtube_url, num_comments], outputs=[output_summary, output_chart, output_table] ) if __name__ == "__main__": demo.launch(share=False)