Update main.py

main.py

@@ -1,31 +1,3 @@
-from contextlib import asynccontextmanager
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, ValidationError
-from fastapi.encoders import jsonable_encoder
-
-# TEXT PREPROCESSING
-# --------------------------------------------------------------------
-import re
-import string
-import nltk
-nltk.download('punkt')
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-from nltk.stem import WordNetLemmatizer
-
-# Function to remove URLs from text
-def remove_urls(text):
-    return re.sub(r'http[s]?://\S+', '', text)
-
-# Function to remove punctuations from text
-def remove_punctuation(text):
-    regular_punct = string.punctuation
-    return str(re.sub(r'['+regular_punct+']', '', str(text)))
-
-# Function to convert the text into lower case
-def lower_case(text):
-    return text.lower()
-
 # Function to lemmatize text
 def lemmatize(text):
     wordnet_lemmatizer = WordNetLemmatizer()
@@ -45,7 +17,7 @@ def preprocess_text(text):
     text = lemmatize(text)
     return text
 
-# Load the model using FastAPI lifespan event so that the model is loaded at the beginning for efficiency
+# Load the model using FastAPI lifespan event so that teh model is loaded at the beginning for efficiency
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # Load the model from HuggingFace transformers library
@@ -56,8 +28,14 @@ async def lifespan(app: FastAPI):
     # Clean up the model and release the resources
     del sentiment_task
 
+description = """
+## Text Classification API 
+This app shows 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):
@@ -66,13 +44,39 @@ class TextInput(BaseModel):
 # Define the welcome endpoint
 @app.get('/')
 async def welcome():
-    # Redirect to the Swagger UI page
-    return RedirectResponse(url="/docs")
-
+    return "Welcome to our Text Classification API"
 
 # Validate input text length
 MAX_TEXT_LENGTH = 1000
 
+# Define the sentiment analysis endpoint 
+@app.post('/analyze/{text}')
+async def classify_text(text_input:TextInput):    
+    try:
+        # Convert input data to JSON serializable dictionary
+        text_input_dict = jsonable_encoder(text_input)
+        # Validate input data using Pydantic model
+        text_data = TextInput(**text_input_dict)  # Convert to Pydantic model
+
+        # Validate input text length
+        if len(text_input.text) > MAX_TEXT_LENGTH:
+            raise HTTPException(status_code=400, detail="Text length exceeds maximum allowed length")
+        elif len(text_input.text) == 0:
+            raise HTTPException(status_code=400, detail="Text cannot be empty")
+    except ValidationError as e:
+        # Handle validation error
+        raise HTTPException(status_code=422, detail=str(e))
+
+    try:
+        # Perform text classification
+        return sentiment_task(preprocess_text(text_input.text))
+    except ValueError as ve:
+        # Handle value error
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        # Handle other server errors
+        raise HTTPException(status_code=500, detail=str(e))
+
 # Define the sentiment analysis endpoint 
 @app.post('/analyze/{text}')
 async def classify_text(text_input:TextInput):