Update app.py

app.py

@@ -6,8 +6,6 @@ import catboost
 from sklearn.impute import SimpleImputer
 import requests
 
-
-
 # Load the saved model and unique values:
 with open("model_and_key_components.pkl", "rb") as f:
     components = pickle.load(f)
@@ -116,25 +114,31 @@ if st.button("Predict"):
         "importance_of_record": float(importance_of_record)
     }
 
-    # Specify the port that the FastAPI API is running on
+    # Send a POST request to the FastAPI server using urllib.request.urlopen()
+    import urllib.request
+    
     api_port = 7860
+    url = urllib.request.URLopener()
+    url.base_url = f"https://rasmodev-income-prediction-fastapi.hf.space/predict/"
+    url.addheaders = [("Content-Type", "application/json")]
+    url.addheaders.append(("Accept", "application/json"))
+    request = urllib.request.Request(url)
+    request.data = json.dumps(user_input).encode("utf-8")
+    response = url.open(request)
     
-    # Send a POST request to the FastAPI server
-    response = requests.post("https://rasmodev-income-prediction-fastapi.hf.space/predict/", json=user_input, port=api_port)
-
     # Check if the request was successful
-    if response.status_code == 200:
-        prediction_data = response.json()
-
+    if response.status == 200:
+        prediction_data = json.loads(response.read().decode("utf-8"))
+    
         # Display prediction result to the user
         st.subheader("Prediction Result")
-
+    
         # Determine income prediction and format message
         if prediction_data['income_prediction'] == "Income over $50K":
             st.success("This individual is predicted to have an income of over $50K.")
         else:
             st.error("This individual is predicted to have an income of under $50K")
-
+    
         # Display prediction probability
         st.subheader("Prediction Probability")
         probability = prediction_data['prediction_probability']