editing README

README.md

@@ -5,5 +5,28 @@ This API is built using **FastAPI** and is designed to perform **sentiment analy
 🌐 **Live Demo**: The API is deployed on **Azure Web Services** and can be accessed at the following link:  
 [Emotion Sense API](https://emotionsense-dvemggcjagcqdxey.uaenorth-01.azurewebsites.net/)
 
-🚨 **Note**:  
-Unfortunately, I can't keep the API live indefinitely as it incurs costs 💸. If the service is down or the link isn't valid anymore, feel free to clone the repository and run it locally to explore its features!
+## POST /predict
+
+This endpoint takes in a text input and returns a sentiment prediction. The sentiment is classified as `positive`, `neutral`, or `negative` based on the analysis of the text.
+
+### Request:
+
+- **URL**: `/predict`
+- **Method**: `POST`
+- **Content-Type**: `application/json`
+- **Request Body**:
+  - The request body should contain a string of text to be analyzed for sentiment.
+
+### Response:
+
+- **Content-Type**: `application/json`
+- **Body**: A JSON object containing a `prediction` field with an integer value:
+  - `0` = neutral
+  - `1` = positive
+  - `2` = negative
+
+#### Example response:
+```json
+{
+    "prediction": 1
+}

main.py

@@ -10,38 +10,28 @@
 import nltk
 import os
 import gdown
-import zipfile
 
 
+# Function to get the model and tokenizer from google drive instead of putting it in the repo
 def download_file_from_google_drive(file_id, output_path):
     url = f'https://drive.google.com/uc?id={file_id}'
     gdown.download(url, output_path, quiet=False)
 
 
-# Example usage:
 download_file_from_google_drive('1qWkyNQXhcwlE-enuY0suIvsOy-5oRIkQ', './COVID_NLP5.keras')
 download_file_from_google_drive('12_AgHa0hiIPLeWQy51yovk8hpN6xzlo5', './tokenizer5.joblib')
 
-# output = 'nltk_data.zip'
-# download_file_from_google_drive('1t5t1bL2EJr1vEY0nMs0x1l50tFZSUXLP', output)
-
-# # Extract the zip file
-# with zipfile.ZipFile(output, 'r') as zip_ref:
-#     zip_ref.extractall()  # Extract to current directory
-
-
 # Define the directory where NLTK data will be stored
 
 nltk_data_dir = os.path.join(os.path.dirname(__file__), "nltk_data")
 
 # Add this directory to NLTK's data path
 nltk.data.path.append(nltk_data_dir)
 
-nltk.download('punkt',download_dir=nltk_data_dir) # At first you have to download these nltk packages.
-nltk.download('stopwords',download_dir=nltk_data_dir)
-nltk.download('wordnet',download_dir=nltk_data_dir)
-nltk.download('punkt_tab',download_dir=nltk_data_dir)
-
+nltk.download('punkt', download_dir=nltk_data_dir)  # At first you have to download these nltk packages.
+nltk.download('stopwords', download_dir=nltk_data_dir)
+nltk.download('wordnet', download_dir=nltk_data_dir)
+nltk.download('punkt_tab', download_dir=nltk_data_dir)
 
 from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer
@@ -100,7 +90,7 @@ def nlp_preprocessing(tweet):
 
 @app.get("/")
 async def root():
-    return {"message": "Hello World"}
+    return {"message": "Welcome to Emotion Sense API"}
 
 
 class PredictRequest(BaseModel):