Update main.py

main.py

@@ -49,15 +49,26 @@ def preprocess_text(text):
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # Load the model from HuggingFace transformers library
+ #   from transformers import pipeline
+    
+#    sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    # Use a pipeline as a high-level helper
     from transformers import pipeline
     global sentiment_task
-    sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    pipe = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions","SamLowe/roberta-base-go_emotions")
+    
     yield
     # Clean up the model and release the resources
     del sentiment_task
 
+description = """
+## Text Classification API 
+Upon input to this app, It will show the sentiment of the text (positive, negative, or neutral).
+Check out the docs for the `/analyze/{text}` endpoint below to try it out!
+"""
+    
 # Initialize the FastAPI app
-app = FastAPI(lifespan=lifespan)
+app = FastAPI(lifespan=lifespan, docs_url="/", description=description)
 
 # Define the input data model
 class TextInput(BaseModel):
@@ -66,7 +77,7 @@ class TextInput(BaseModel):
 # Define the welcome endpoint
 @app.get('/')
 async def welcome():
-    return "Welcome to our Text Classification API"
+    return "Welcome to our First Emotion  Classification API"
 
 # Validate input text length
 MAX_TEXT_LENGTH = 1000