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ANN_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0acee37b4db5459a3389bdbf7c72db2a886894142d743f83394f01dec0c79c2b
+size 5212

app.py

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+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.")

requirements.txt

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+joblib==1.4.2
+numpy==2.2.1
+pandas==2.2.3
+scikit-learn==1.6.1
+streamlit==1.41.1
+torch == 2.5.1