@app.post("/analyze")
async def analyze(request: Request):
    data = await request.json()
    text = preprocess(data.get("text", ""))

    if not text.strip():
        return {"error": "Empty input"}

    # Tokenize to check length without truncating
    tokenized = tokenizer(text, return_tensors='pt', add_special_tokens=True)
    num_tokens = tokenized.input_ids.shape[1]

    if num_tokens <= 512:
        # ✅ Use direct inference for short inputs
        encoded_input = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
        output = model(**encoded_input)
        scores = output[0][0].detach().numpy()
        probs = softmax(scores)

        result = [
            {"label": config.id2label[i], "score": round(float(probs[i]), 4)}
            for i in probs.argsort()[::-1]
        ]

        return {"result": result}

    else:
        # ✅ Long input: Split into chunks of ~500 words
        max_words = 500
        words = text.split()
        chunks = [" ".join(words[i:i + max_words]) for i in range(0, len(words), max_words)]

        all_scores = []
        for chunk in chunks:
            encoded_input = tokenizer(chunk, return_tensors='pt', truncation=True, padding=True, max_length=512)
            output = model(**encoded_input)
            scores = output[0][0].detach().numpy()
            probs = softmax(scores)
            all_scores.append(probs)

        # Average softmax scores
        avg_scores = np.mean(all_scores, axis=0)

        result = [
            {"label": config.id2label[i], "score": round(float(avg_scores[i]), 4)}
            for i in avg_scores.argsort()[::-1]
        ]

        return {"result": result}