Create main.py

main.py

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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}"
+