Keep getting: "detail": "Error in emotion analysis: list indices must be integers or slices, not str"
@app.get("/sentiment-analysis")
def sentiment_analysis(url: str):
    """
    Fetches webpage content using 'requests'.
    Uses 'BeautifulSoup' to extract relevant textual content.
    Applies 'NLTK' and 'SentimentIntensityAnalyzer' to determine sentiment scores.
    Also uses a pretrained model from Hugging Face's Transformers for emotion analysis.
    """

    try:
        # Fetching webpage content
        response = requests.get(url)
        response.raise_for_status()
    except requests.RequestException as e:
        raise HTTPException(status_code=400, detail=f"Error fetching the URL: {e}")

    # Parsing and extracting text from HTML
    soup = BeautifulSoup(response.text, 'html.parser')
    text = soup.get_text().strip()
    sia = SentimentIntensityAnalyzer()

    # NLTK Sentiment Analysis
    sentiment_scores = sia.polarity_scores(text)

    # Transformers Emotion Analysis
    try:
        # Tokenize and split text into chunks within the token limit
        max_length = 512  # Model's token limit
        tokens = tokenizer.encode(text, add_special_tokens=True, truncation=True, max_length=max_length)
        chunk_size = max_length - 2  # Account for special tokens [CLS] and [SEP]
        chunks = [tokens[i:i + chunk_size] for i in range(0, len(tokens), chunk_size)]
        emotions_aggregated = {}

        for chunk in chunks:
            # Convert chunk back to string
            chunk_text = tokenizer.decode(chunk, skip_special_tokens=True)
            chunk_outputs = classifier(chunk_text)
            for item in chunk_outputs:
                label = item['label']  # Corrected access to the dictionary
                score = item['score']
                emotions_aggregated[label] = emotions_aggregated.get(label, 0) + score

        # Average the scores
        for emotion in emotions_aggregated:
            emotions_aggregated[emotion] /= len(chunks)



    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error in emotion analysis: {e}")

    # Combining results
    return {
        "sentiment_scores": sentiment_scores,
        "emotion_analysis": emotions_aggregated
    }“detail”: “Error in emotion analysis: list indices must be integers or slices, not str”I am really new to the transformers architecture so much of this is probably going over my head, what am I missing here?