app.py

from fastapi import FastAPI
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import onnxruntime as ort
from transformers import AutoTokenizer
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
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import time
import torch

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# Load the tokenizer and ONNX model
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
onnx_model_path = "D:/demodeploy/sentiment_model.onnx"
onnx_session = ort.InferenceSession(onnx_model_path)
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# Load the tokenizer and model directly
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
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app = FastAPI()

def preprocess_tweet(tweet: str) -> str:
    tweet_words = []
    for word in tweet.split(' '):
        if word.startswith('@') and len(word) > 1:
            word = '@user'
        elif word.startswith('http'):
            word = "http"
        tweet_words.append(word)
    return " ".join(tweet_words)

@app.get("/")
def home():
    return {"message": "Welcome to the sentiment analysis API"}

@app.get("/analyze")
def analyze_sentiment(tweet: str):
    # Preprocess the tweet
    tweet_proc = preprocess_tweet(tweet)

    # Measure the time taken for the inference
    start_time = time.time()

    # Tokenize the input tweet
    inputs = tokenizer(tweet_proc, return_tensors="pt")
    input_ids = inputs["input_ids"].numpy()
    attention_mask = inputs["attention_mask"].numpy()

    # Perform the inference using ONNX
    onnx_inputs = {
        'input_ids': input_ids,
        'attention_mask': attention_mask
    }
    outputs = onnx_session.run(None, onnx_inputs)

    # Calculate the inference time
    inference_time = time.time() - start_time

    # Get the probabilities from the logits
    logits = outputs[0]
    probabilities = torch.softmax(torch.tensor(logits), dim=1)

    # Get the label with the highest probability
    max_prob, max_index = torch.max(probabilities, dim=1)

    # Map the labels to desired names
    label_map = {
        0: "Negative",
        1: "Neutral",
        2: "Positive"
    }

    # Get the highest label and its corresponding score
    highest_label = label_map[max_index.item()]
    highest_score = round(max_prob.item(), 4)

    # Return the original tweet, the label with the highest score, and the inference time
    return {
        "text": tweet,
        "label": highest_label,
        "score": highest_score,
        "inference_time": round(inference_time, 4)  # In seconds
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    }
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    }
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