# 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/twitter-roberta-base-sentimental-analysis-of-covid-tweets"
# model = AutoModelForSequenceClassification.from_pretrained(model_name)
# tokenizer = AutoTokenizer.from_pretrained(model_name)

# sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# app = FastAPI()

# @app.get("/")
# async def read_root():
#     return {"message": "Sentiment Analysis API using FastAPI"}

# @app.get("/analyze-sentiment/")
# async def analyze_sentiment(text: str = Query(..., description="Text for sentiment analysis")):
#     result = sentiment(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"

#     response = {
#         "sentiment": sentiment_label.capitalize(),
#         "score": sentiment_score
#     }

#     return response

# if _name_ == "_main_":
#     import uvicorn
#     uvicorn.run(app, host="127.0.0.1", port=7860)


# 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"),
# ):
    
#     input_data = [Text]

#     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)