call the preprocess api in the prediction

app.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel
 from typing import Literal, List, Union
 from fastapi import FastAPI, File, UploadFile
 import joblib
+import requests
 
 description = """
 Welcome to this offensive speech detection API. 
@@ -99,8 +100,24 @@ async def predict(predictionFeatures: PredictionFeatures):
     ```
     """
 
+ 
+
+    #Call the API
+    url = "https://llepogam-hate-speech-detection-api.hf.space/preprocess"
+    headers = {
+        "accept": "application/json",
+        "Content-Type": "application/json"
+    }
+
+    data_to_preprocessed = {
+        "tweet": predictionFeatures.Text
+    }
+
+    response = requests.post(url, headers=headers, json=data_to_preprocessed)
+
+
     # Convert input into a DataFrame
-    list_text = [predictionFeatures.Text]
+    list_text = [response.json()['text_clean']['0']]
 
     # Load model from MLflow
     logged_model = 'runs:/227d2f8e431d40d6b5231add3a00d048/hate_speech_detection'
@@ -137,7 +154,28 @@ async def predict(preprocessingFeatures: PreprocessingFeatures):
     ### Output
     Returns a dictionary with the following keys:
     - `tweet` (str): Initial tweet.
-    - `text_clean` (str): Preprocessed tweets after removal of punctation and stop words and text lemmatization .
+    - `text_clean` (str): Preprocessed tweets after removal of punctation and stop words and text lemmatization.
+
+    ### Example Usage
+    To use this endpoint, send a POST request as follows:
+
+    ```python
+    import requests
+
+    url = "https://llepogam-hate-speech-detection-api.hf.space/preprocess"
+    headers = {
+        "accept": "application/json",
+        "Content-Type": "application/json"
+    }
+
+    data = {
+        "tweet": "@user this is the tweet which i want to preprocess ! #machinelearning #prediction"
+    }
+
+    response = requests.post(url, headers=headers, json=data)
+    print(response.json())
+    ```
+    
 
     """