Update app.py

app.py

@@ -159,6 +159,7 @@ st.markdown(
         h1, h2, h3, h4, h5, h6 {
             color: #00f7ff !important; /* Headings to cyan */
         }
+
         /* Styles for loader */
         .loader {
             border: 5px solid #f3f3f3;
@@ -168,6 +169,7 @@ st.markdown(
             height: 30px;
             animation: spin 2s linear infinite;
         }
+
         @keyframes spin {
             0% { transform: rotate(0deg); }
             100% { transform: rotate(360deg); }
@@ -180,18 +182,18 @@ st.markdown(
 # --- Image Loading ---
 @st.cache_data(ttl=3600)
 async def load_image(image_url):
-    """Loads an image from a URL asynchronously."""
+    """Loads an image from a URL asynchronously and returns bytes."""
     try:
         response = requests.get(image_url, stream=True)
         response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
-        return BytesIO(response.content)  # Return image data as a BytesIO object
+        return response.content  # Return image data as bytes
     except requests.exceptions.RequestException as e:
         st.error(f"Error loading image: {e}")
-        return None  # Handle errors gracefully
+        return None
 
 async def set_background():
     """Sets the background image."""
-    image_url = "https://images.unsplash.com/photo-1504821618514-8c1b6e408ca8?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1949&q=80"  # Replace with actual URL
+    image_url = "https://images.unsplash.com/photo-1504821618514-8c1b6e408ca8?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1949&q=80"
     image_data = await load_image(image_url)
 
     if image_data:
@@ -199,7 +201,7 @@ async def set_background():
             f"""
             <style>
             .stApp {{
-                background-image: url(data:image/{"jpeg"};base64,{image_data.getvalue().hex()});
+                background-image: url(data:image/{"jpeg"};base64,{image_data.hex()});
                 background-size: cover;
             }}
             </style>
@@ -277,8 +279,7 @@ def animated_progress_bar(progress_var, message="Processing..."):
         time.sleep(0.01) # reduced sleep timer as its getting too long
 
 # --- Main App Logic ---
-
-if app_mode == "Data Upload": #Check
+if app_mode == "Data Upload":
     st.title("📤 Data Upload & Analysis")
 
     uploaded_file = st.file_uploader("Upload Dataset", type=["csv", "xlsx"])
@@ -304,7 +305,7 @@ if app_mode == "Data Upload": #Check
                     pr = generate_profile(df)
                     st_profile_report(pr)
 
-elif app_mode == "Smart Cleaning": #Check
+elif app_mode == "Smart Cleaning":
     st.title("🧼 Intelligent Data Cleaning")
 
     if st.session_state.raw_data is not None:
@@ -413,7 +414,7 @@ elif app_mode == "Smart Cleaning": #Check
         with col2:
             st.write("Cleaned Data", df.head(3))
 
-elif app_mode == "Advanced EDA": #Check
+elif app_mode == "Advanced EDA":
     st.title("🔍 Advanced Exploratory Analysis")
 
     if st.session_state.cleaned_data is not None:
@@ -475,7 +476,7 @@ elif app_mode == "Advanced EDA": #Check
             except Exception as e:
                 st.error(f"Error generating plot: {e}")
 
-elif app_mode == "Model Training": #Check
+elif app_mode == "Model Training":
     st.title("🤖 Model Training Studio")
 
     if st.session_state.cleaned_data is not None:
@@ -646,8 +647,8 @@ elif app_mode == "Model Training": #Check
                 except Exception as e:
                     st.error(f"Error during training: {e}")
 
-#Predictions
-elif app_mode == "Predictions": #Check
+# Predictions Section
+elif app_mode == "Predictions":
     st.title("🔮 Make Predictions")
 
     if st.session_state.model is not None and st.session_state.preprocessor is not None:
@@ -664,7 +665,6 @@ elif app_mode == "Predictions": #Check
 
         if st.button("Predict"):
             try:
-
                 input_df = pd.DataFrame([input_data])
                 # Preprocess input
                 input_processed = preprocessor.transform(input_df)
@@ -684,8 +684,9 @@ elif app_mode == "Predictions": #Check
                 st.error(f"Error during prediction: {e}")
     else:
         st.warning("Please train a model first.")
-        
-elif app_mode == "Visualization Lab":
+
+
+    elif app_mode == "Visualization Lab":
     st.title("📊 Advanced Visualization Lab")
 
     if st.session_state.cleaned_data is not None: