Update app.py

app.py

@@ -1,54 +1,91 @@
-import pandas as pd
-import numpy as np
-import re
-import streamlit as st 
-import joblib
-import numpy as np
-import pandas as pd
-
-
-html_temp = """
-    <div style="background-color:black;padding:10px">
-    <h2 style="color:white;text-align:center;">Chat GPT Review Prediction  </h2>
-    </div>
-    """
-st.markdown(html_temp, unsafe_allow_html=True)
-
-image_url="https://storage.googleapis.com/kaggle-datasets-images/6377125/10302664/91e3eb67027ab3122886b971613e7c2f/dataset-cover.jpg?t=2024-12-26-10-34-17"
-
-st.image(image_url, use_container_width=True)
-
-
-
-input_txt=st.text_input("Enter the Review")
-
-
-# preprocess the text
-
-def preprocess_text(text):
-    text = text.lower()  
-    text = re.sub(r'\d+', '', text)  
-    text = re.sub(r'[^\w\s]', '', text)  
-    text = re.sub(r'\s+', ' ', text)  
-
-    return text
-
-# loading the models
-loaded_tfidf = joblib.load("tfidf_model.joblib")
-model = joblib.load("chat_review_model.joblib")
-
-# processing
-if input:
-    test=preprocess_text(input_txt)
-    label=loaded_tfidf.transform([test])
-
-# predict and display
-if st.button("Submit"):
-    predict=model.predict(label)[0]
-        
-    if predict==1:
-        st.write("Its is a Good Review")
-        
-    elif predict==0:
-        st.write("Its is a Bad Review")
-
+import pandas as pd
+import numpy as np
+import re
+import streamlit as st 
+import joblib
+import numpy as np
+import pandas as pd
+
+st.markdown(f"""
+    <style>
+        /* Set the background image for the entire app */
+        .stApp {{
+            background-color: #BA944E;
+            background-size: 100px;
+            background-repeat:no;
+            background-attachment: auto;
+            background-position:full;
+        }}
+    </style>
+    """, unsafe_allow_html=True)
+    
+
+html_temp = """
+    <div style="background-color:black;padding:10px">
+    <h2 style="color:white;text-align:center;">Chat GPT Review Prediction  </h2>
+    </div>
+    """
+st.markdown(html_temp, unsafe_allow_html=True)
+
+image_url="https://storage.googleapis.com/kaggle-datasets-images/6377125/10302664/91e3eb67027ab3122886b971613e7c2f/dataset-cover.jpg?t=2024-12-26-10-34-17"
+
+st.image(image_url, use_container_width=True)
+
+
+
+
+input_txt=st.text_input("Enter the Review")
+
+
+# preprocess the text
+
+def preprocess_text(text):
+    text = text.lower()  
+    text = re.sub(r'\d+', '', text)  
+    text = re.sub(r'[^\w\s]', '', text)  
+    text = re.sub(r'\s+', ' ', text)  
+
+    return text
+
+# loading the models
+loaded_tfidf = joblib.load("tfidf_model.joblib")
+model = joblib.load("chat_review_model.joblib")
+
+# processing
+if input:
+    test=preprocess_text(input_txt)
+    label=loaded_tfidf.transform([test])
+
+# Predict and display the result
+if st.button("Submit"):
+    try:
+        # Get prediction from the model
+        prediction = model.predict(label)[0]
+
+        # Define messages and colors
+        review_status = {
+            1: ("✅ It is a Good Review!", "#32CD32"),  # Green
+            0: ("❌ It is a Bad Review!", "#FF4500")   # Red-Orange
+        }
+
+        # Get message and color based on prediction
+        message, color = review_status.get(prediction, ("❓ Unknown Prediction", "#808080"))
+
+        # Display styled result
+        st.markdown(f"""
+            <div style="
+                padding: 15px; 
+                background-color: {color}; 
+                border-radius: 10px; 
+                text-align: center; 
+                font-size: 18px; 
+                font-weight: bold; 
+                color: white;">
+                {message}
+            </div>
+        """, unsafe_allow_html=True)
+
+    except Exception as e:
+        st.error(f"⚠️ Error in prediction: {e}")
+
+    st.write("")