Update main.py

main.py

@@ -1,3 +1,31 @@
+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()
@@ -17,7 +45,7 @@ def preprocess_text(text):
     text = lemmatize(text)
     return text
 
-# Load the model using FastAPI lifespan event so that teh model is loaded at the beginning for efficiency
+# Load the model using FastAPI lifespan event so that the model is loaded at the beginning for efficiency
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # Load the model from HuggingFace transformers library
@@ -28,14 +56,8 @@ 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, docs_url="/", description=description)
+app = FastAPI(lifespan=lifespan)
 
 # Define the input data model
 class TextInput(BaseModel):
@@ -46,40 +68,9 @@ class TextInput(BaseModel):
 async def welcome():
     return "Welcome to our Text Classification API"
 
- # Redirect to the Swagger UI page
-    return RedirectResponse(url="/docs")
-
 # 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):    
@@ -129,8 +120,7 @@ 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