Add main.py

main.py

@@ -0,0 +1,53 @@
+from fastapi import FastAPI, Query
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+app = FastAPI()
+
+# Load the pre-trained model and tokenizer
+model_name = "imalexianne/Movie_Review_Roberta"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# tokenizer = AutoTokenizer.from_pretrained("username/model_name")
+
+# Create a sentiment analysis pipeline
+sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+# Create a dictionary to map sentiment labels to positive and negative strings
+sentiment_label_mapping = {
+    "LABEL_1": "positive",
+    "LABEL_0": "negative",
+}
+
+# Define a request body model
+class SentimentRequest(BaseModel):
+    text: str
+
+# Define a response model
+class SentimentResponse(BaseModel):
+    sentiment: str  # 1 for positive, 0 for negative
+    score: float
+@app.get("/")
+def read_root():
+    explanation = {
+        'message': "Welcome to the Movie Review Sentiment Prediction App",
+        'description': "This API allows you to predict Movie Review Sentiment based on a given text",
+        'usage': "Submit a POST request to /predict with text to make predictions.",
+        
+    }
+    return explanation
+# Create an endpoint for sentiment analysis with query parameter
+@app.get("/sentiment/")
+async def analyze_sentiment(text: str = Query(..., description="Input text for sentiment analysis")):
+    result = sentiment(text)
+    sentiment_label = result[0]["label"]
+    sentiment_score = result[0]["score"]
+    
+    sentiment_value = sentiment_label_mapping.get(sentiment_label, -1)  # Default to -1 for unknown labels
+    
+    return SentimentResponse(sentiment=sentiment_value, score=sentiment_score)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+