Create main.py

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, validator
+from transformers import pipeline
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Load the sentiment analysis pipeline
+sentiment_model = pipeline("text-classification", model="MarieAngeA13/Sentiment-Analysis-BERT")
+
+
+# Define a Pydantic model for the input data
+class Text(BaseModel):
+    text: str
+
+    @validator('text')
+    def must_not_be_blank(cls, value):
+        if not value.strip():  # Check if the text is not just whitespace
+            raise ValueError('Text must not be empty or just whitespace')
+        return value
+
+@app.get("/")
+def read_root():
+    return {"Hello": "Welcome to our Sentiment Analysis API, type '/docs' after the <URL> to access the Swagger UI"}
+
+@app.post("/analyze")
+def analyze(text: Text):
+    try:
+        # Process the text through the sentiment analysis model
+        result = sentiment_model(text.text)
+        return {"result": result}
+    except ValueError as ve:
+        # Handle validation errors, which occur when text is empty or just whitespace
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        # Handle all other kinds of unexpected errors
+        raise HTTPException(status_code=500, detail="An error occurred during the analysis.")