Update API.py

API.py

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+# Now, trying using the youtube video id
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import pandas as pd
+import googleapiclient.discovery
+
+app = FastAPI()
+
+api_service_name = "youtube"
+api_version = "v3"
+DEVELOPER_KEY = "AIzaSyC4Vx8G6nm3Ow9xq7NluTuCCJ1d_5w4YPE"  # Replace with your actual YouTube API key
+
+youtube = googleapiclient.discovery.build(api_service_name, api_version, developerKey=DEVELOPER_KEY)
+
+class SingleInput(BaseModel):
+    video_id: str
+
+# Load the BERT tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+
+def scrape_comments(video_id):
+    request = youtube.commentThreads().list(
+        part="snippet",
+        videoId=video_id,
+        maxResults=100  # You can adjust the maximum number of comments to fetch
+    )
+    response = request.execute()
+
+    comments = []
+
+    for item in response['items']:
+        comment = item['snippet']['topLevelComment']['snippet']
+        comments.append(comment['textDisplay'])
+
+    comments_df = pd.DataFrame(comments, columns=['comment'])
+    return comments_df
+
+@app.post("/analyze_sentiment/")
+def analyze_sentiment_endpoint(data: SingleInput):
+    video_id = data.video_id
+
+    comments_df = scrape_comments(video_id)
+
+    if comments_df.empty:
+        raise HTTPException(status_code=400, detail="No comments found for the provided video ID")
+
+    tokenized_comments = tokenizer(list(comments_df['comment']), padding=True, truncation=True, return_tensors="pt")
+
+    # Perform sentiment analysis
+    with torch.no_grad():
+        outputs = model(**tokenized_comments)
+        logits = outputs.logits
+
+    # Determine sentiment for each comment
+    sentiment_ids = torch.argmax(logits, dim=1).tolist()
+    sentiment_labels = []
+    for sentiment_id in sentiment_ids:
+        if sentiment_id == 2:
+            sentiment_labels.append("Positive")
+        elif sentiment_id == 0:
+            sentiment_labels.append("Negative")
+        else:
+            sentiment_labels.append("Neutral")
+
+    sentiment_counts = {
+        "positive": sentiment_labels.count("Positive"),
+        "negative": sentiment_labels.count("Negative"),
+        "neutral": sentiment_labels.count("Neutral")
+    }
+
+    return {"sentiment_counts": sentiment_counts, "comments_count": len(comments_df)}