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from fastapi import FastAPI, Query, Request, HTTPException
import pandas as pd
import transformers as pipeline
from transformers import AutoTokenizer,AutoModelForSequenceClassification
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "Sonny4Sonnix/Movie_Sentiments_Analysis_with_FastAPI" # Replace with the name of the pre-trained model you want to use
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
app = FastAPI()
@app.get("/")
async def read_root():
return {"message": "Welcome to the Sepsis Prediction using FastAPI"}
def classify(prediction):
if prediction == 0:
return "Sentence is positive"
else:
return "Sentence is negative"
@app.post("/predict/")
async def predict_sepsis(
request: Request,
Text: float = Query(..., description="Please type a sentence"),
# pl: float = Query(..., description="Blood Work Result-1 (mu U/ml)"),
# pr: float = Query(..., description="Blood Pressure (mm Hg)"),
# sk: float = Query(..., description="Blood Work Result-2 (mm)"),
# ts: float = Query(..., description="Blood Work Result-3 (mu U/ml)"),
# m11: float = Query(..., description="Body mass index (weight in kg/(height in m)^2"),
# bd2: float = Query(..., description="Blood Work Result-4 (mu U/ml)"),
# age: int = Query(..., description="Patient's age (years)")
):
#input_data = [prg, pl, pr, sk, ts, m11, bd2, age]
input_data = [Text]
# input_df = pd.DataFrame([input_data], columns=[
# "Plasma glucose", "Blood Work Result-1", "Blood Pressure",
# "Blood Work Result-2", "Blood Work Result-3",
# "Body mass index", "Blood Work Result-4", "Age"
input_df = pd.DataFrame([input_data], columns=[
"Text"
])
pred = model.predict(input_df)
output = classify(pred[0])
response = {
"prediction": output
}
return response
# Run the app using Uvicorn
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="127.0.0.1", port=7860)
sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
# def get_sentiment(input_text):
# result = sentiment(input_text)
# sentiment_label = result[0]['label']
# sentiment_score = result[0]['score']
# if sentiment_label == 'LABEL_1':
# sentiment_label = "positive"
# elif sentiment_label == 'LABEL_0':
# sentiment_label = "neutral"
# else:
# sentiment_label = "negative"
# return f"Sentiment: {sentiment_label.capitalize()}, Score: {sentiment_score:.2f}"
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