Update app.py

app.py

@@ -2,6 +2,11 @@ import streamlit as st
 from tensorflow.keras.models import load_model
 import pickle
 import numpy as np
+import os
+import google.generativeai as genai
+
+# Configure the generative AI with the API key
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
 # Load the saved models
 with open('rf_model.pkl', 'rb') as file:
@@ -22,15 +27,27 @@ def make_deep_prediction(model, input_data):
 # Define the function to calculate GPA
 def calculate_gpa(total_score):
     if total_score >= 70:
-        return 'A (5 points)'
+        return 'A (5 points)', total_score
     elif total_score >= 60:
-        return 'B (4 points)'
+        return 'B (4 points)', total_score
     elif total_score >= 50:
-        return 'C (3 points)'
+        return 'C (3 points)', total_score
     elif total_score >= 45:
-        return 'D (2 points)'
+        return 'D (2 points)', total_score
+    else:
+        return 'F (0 points)', total_score
+
+# Function to generate grade-based recommendations using Gemini API
+def generate_grade_recommendations(grade):
+    if grade >= 70:
+        grade_desc = "good grade"
     else:
-        return 'F (0 points)'
+        grade_desc = "bad grade"
+
+    input_prompt = f"The student has a {grade_desc}. What recommendations do you have for them?"
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(input_prompt, generation_config=genai.types.GenerationConfig(max_output_tokens=400))
+    return response.text
 
 # Create the Streamlit app
 def main():
@@ -75,8 +92,13 @@ def main():
         st.write(f"Total Score: {total_score:.2f}")
         
         # Calculate GPA
-        gpa = calculate_gpa(total_score)
+        gpa, numeric_score = calculate_gpa(total_score)
         st.write(f"Predicted GPA: {gpa}")
 
+        # Generate recommendations based on GPA
+        recommendations = generate_grade_recommendations(numeric_score)
+        st.subheader("Recommendations Based on Grade:")
+        st.write(recommendations)
+
 if __name__ == '__main__':
     main()