Update app.py

app.py

@@ -1,51 +1,86 @@
-import streamlit as st
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-st.title("Placement Analysis")
-# Define the same model architecture
-class ANN_Model(nn.Module):
-    def __init__(self, input_cols=10, hidden1=20, hidden2=20, output=1):  
-        super().__init__()
-        self.f_connected1 = nn.Linear(input_cols, hidden1)
-        self.f_connected2 = nn.Linear(hidden1, hidden2)
-        self.out = nn.Linear(hidden2, output)
-
-    def forward(self, x):
-        x = F.relu(self.f_connected1(x))
-        x = F.relu(self.f_connected2(x))
-        x = torch.sigmoid(self.out(x))  # Sigmoid for binary classification
-        return x
-
-# Load the model
-model = ANN_Model()  # Initialize the model
-model.load_state_dict(torch.load("ANN_model.pth"))  # Load saved weights
-model.eval()  # Set to evaluation mode
-
-cgpa = st.number_input("Enter your CGPA", min_value=0.0, max_value=10.0, step=0.01)
-internships = st.number_input("Number of Internships", min_value=0, max_value=10, step=1)
-projects = st.number_input("Number of Projects", min_value=0, max_value=20, step=1)
-certifications = st.selectbox("Do you have certifications?", ["Yes", "No"])
-certifications = 1 if certifications =="Yes" else 0
-AptitudeTestScore = st.number_input("Aptitude Test Score", min_value=0, max_value=100, step=1)
-SoftSkillsRating = st.number_input("Soft Skills Rating",min_value=0,max_value=5)
-ExtracurricularActivities = st.selectbox("Extracurricular Activities",["Yes","No"])
-ExtracurricularActivities = 1 if ExtracurricularActivities=="Yes" else 0
-PlacementTraining = st.selectbox("Placement Training",["Yes","No"])
-PlacementTraining = 1 if PlacementTraining=="Yes" else 0
-SSC_Marks = st.number_input("SSC Marks", min_value=0, max_value=100, step=1)
-HSC_Marks = st.number_input("HSC Marks", min_value=0, max_value=100, step=1)
-# Predict Button
-if st.button("Predict Placement"):
-    # Prepare input for model
-    input_data = [cgpa, internships, projects,certifications,AptitudeTestScore,SoftSkillsRating,
-                            ExtracurricularActivities,PlacementTraining,SSC_Marks,HSC_Marks ]
-    # # Example: Making a prediction
-    with torch.no_grad():
-        output = model(torch.tensor(input_data)).numpy()
-        if output >= 0.5:
-            st.success("🎉 Congratulations! You are likely to get placed.")
-        else:
-            st.error("⚠️ You might need to improve your profile for better chances.")
+import streamlit as st
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import base64
+
+# Function to set background image
+def set_background(image_file):
+    with open(image_file, "rb") as image:
+        encoded_string = base64.b64encode(image.read()).decode()
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background-image: url(data:image/png;base64,{encoded_string});
+            background-size: cover;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Set the background image
+set_background('path_to_your_image.png')
+
+# Custom title with blue color
+st.markdown("<h1 style='color: blue;'>Placement Analysis</h1>", unsafe_allow_html=True)
+
+# Define the same model architecture
+class ANN_Model(nn.Module):
+    def __init__(self, input_cols=10, hidden1=20, hidden2=20, output=1):  
+        super().__init__()
+        self.f_connected1 = nn.Linear(input_cols, hidden1)
+        self.f_connected2 = nn.Linear(hidden1, hidden2)
+        self.out = nn.Linear(hidden2, output)
+
+    def forward(self, x):
+        x = F.relu(self.f_connected1(x))
+        x = F.relu(self.f_connected2(x))
+        x = torch.sigmoid(self.out(x))  # Sigmoid for binary classification
+        return x
+
+# Load the model
+model = ANN_Model()  # Initialize the model
+model.load_state_dict(torch.load("ANN_model.pth"))  # Load saved weights
+model.eval()  # Set to evaluation mode
+
+# User inputs
+cgpa = st.number_input("Enter your CGPA", min_value=0.0, max_value=10.0, step=0.01)
+internships = st.number_input("Number of Internships", min_value=0, max_value=10, step=1)
+projects = st.number_input("Number of Projects", min_value=0, max_value=20, step=1)
+certifications = st.selectbox("Do you have certifications?", ["Yes", "No"])
+certifications = 1 if certifications == "Yes" else 0
+AptitudeTestScore = st.number_input("Aptitude Test Score", min_value=0, max_value=100, step=1)
+SoftSkillsRating = st.number_input("Soft Skills Rating", min_value=0, max_value=5)
+ExtracurricularActivities = st.selectbox("Extracurricular Activities", ["Yes", "No"])
+ExtracurricularActivities = 1 if ExtracurricularActivities == "Yes" else 0
+PlacementTraining = st.selectbox("Placement Training", ["Yes", "No"])
+PlacementTraining = 1 if PlacementTraining == "Yes" else 0
+SSC_Marks = st.number_input("SSC Marks", min_value=0, max_value=100, step=1)
+HSC_Marks = st.number_input("HSC Marks", min_value=0, max_value=100, step=1)
+
+# Predict Button
+if st.button("Predict Placement"):
+    # Prepare input for model
+    input_data = [cgpa, internships, projects, certifications, AptitudeTestScore, SoftSkillsRating,
+                  ExtracurricularActivities, PlacementTraining, SSC_Marks, HSC_Marks]
+    # Example: Making a prediction
+    with torch.no_grad():
+        output = model(torch.tensor(input_data)).numpy()
+        if output >= 0.5:
+            st.markdown(
+                """
+                <div style='background-color: #d4edda; padding: 10px; border-radius: 5px;'>
+                    <h4 style='color: #155724;'>🎉 Congratulations! You are likely to get placed.</h4>
+                </div>
+                """,
+                unsafe_allow_html=True
+            )
+        else:
+            st.markdown(
+                """
+                <div style='background-color: #f8d7da;
+::contentReference[oaicite:2]{index=2}
+