Update app.py

app.py

@@ -1,48 +1,48 @@
-
-# Hours Studied	Previous Scores	Extracurricular Activities	Sleep Hours	Sample Question Papers Practiced	Performance Index\
-
-import numpy as np
-import joblib
-import streamlit as st
-
-
-
-# Load the trained model
-model = joblib.load(r"C:\Users\asgar\Downloads\machine learning HN\models\student_performance_model.h5")
-
-def predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question):
-    "predict the student marks based on the input data"
-    input_data = np.array([[Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question]])
-    prediction= model.predict(input_data)
-    return round(float(prediction),2)
-
-def main():
-    st.title("Student Marks Predictor")
-
-    #Input data
-    name = st.text_input("Enter your name")
-    Hours_studied = st.number_input("Enter the number of Hours you Studied", min_value=0.0,max_value=20.0,value=0.0)
-    Previous_Score = st.number_input("Enter your Previous exam Score", min_value=0,max_value=100,value=0)
-    Extracurriculum_Activivities = st.number_input("Enter the number extracurriculum activities you have done",min_value=0,max_value=10,value=0)
-    Sleep_Hours = st.number_input("Enter the number of hours you slept",min_value=0.0,max_value=12.0,value=0.0)
-    Sample_Question = st.number_input("Enter the number of Sample Question you have practiced",min_value=0,max_value=50,value=0)
-
-    # predict
-    st.sidebar.write(f"# hi {name}")
-    st.sidebar.write("##### i am a helpful students marks predictor here to assist you in predicting your marks")
-    if st.button("Predict"):
-        prediction = predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question)
-
-        # Display the predictions
-        if prediction >=90:
-            st.success(f"{name} You have a high chances of passing with the the exceptional marks of {prediction} marks keep it up")
-        elif prediction >=35:
-            st.success(f"{name} You have chances of Passing with {prediction} marks try to get 90+")
-        else:
-            st.error(f"{name} You have a very high chances of failing")    
-
-
-
-
-if __name__=="__main__":
-    main()      
+
+# Hours Studied	Previous Scores	Extracurricular Activities	Sleep Hours	Sample Question Papers Practiced	Performance Index\
+
+import numpy as np
+import joblib
+import streamlit as st
+
+
+
+# Load the trained model
+model = joblib.load("student_performance_model.h5")
+
+def predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question):
+    "predict the student marks based on the input data"
+    input_data = np.array([[Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question]])
+    prediction= model.predict(input_data)
+    return round(float(prediction),2)
+
+def main():
+    st.title("Student Marks Predictor")
+
+    #Input data
+    name = st.text_input("Enter your name")
+    Hours_studied = st.number_input("Enter the number of Hours you Studied", min_value=0.0,max_value=20.0,value=0.0)
+    Previous_Score = st.number_input("Enter your Previous exam Score", min_value=0,max_value=100,value=0)
+    Extracurriculum_Activivities = st.number_input("Enter the number extracurriculum activities you have done",min_value=0,max_value=10,value=0)
+    Sleep_Hours = st.number_input("Enter the number of hours you slept",min_value=0.0,max_value=12.0,value=0.0)
+    Sample_Question = st.number_input("Enter the number of Sample Question you have practiced",min_value=0,max_value=50,value=0)
+
+    # predict
+    st.sidebar.write(f"# hi {name}")
+    st.sidebar.write("##### i am a helpful students marks predictor here to assist you in predicting your marks")
+    if st.button("Predict"):
+        prediction = predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question)
+
+        # Display the predictions
+        if prediction >=90:
+            st.success(f"{name} You have a high chances of passing with the the exceptional marks of {prediction} marks keep it up")
+        elif prediction >=35:
+            st.success(f"{name} You have chances of Passing with {prediction} marks try to get 90+")
+        else:
+            st.error(f"{name} You have a very high chances of failing")    
+
+
+
+
+if __name__=="__main__":
+    main()