Create main.py

main.py

@@ -0,0 +1,82 @@
+import streamlit as st
+from app_config import AppConfig
+from data_processor import DataProcessor
+from visualization import Visualization
+from ai_analysis import AIAnalysis
+
+def main():
+    # Initialize the app configuration
+    app_config = AppConfig()
+
+    # Initialize the data processor
+    data_processor = DataProcessor()
+
+    # Initialize the visualization handler
+    visualization = Visualization()
+
+    # Initialize the AI analysis handler
+    ai_analysis = AIAnalysis(data_processor.client)
+
+    st.title("Intervention Program Analysis")
+
+    # File uploader
+    uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"])
+
+    if uploaded_file is not None:
+        try:
+            # Read the Excel file into a DataFrame
+            df = data_processor.read_excel(uploaded_file)
+
+            # Format the session data
+            df = data_processor.format_session_data(df)
+
+            # Replace student names with initials
+            df = data_processor.replace_student_names_with_initials(df)
+
+            st.subheader("Uploaded Data")
+            st.write(df)
+
+            # Ensure expected column is available
+            if DataProcessor.INTERVENTION_COLUMN not in df.columns:
+                st.error(f"Expected column '{DataProcessor.INTERVENTION_COLUMN}' not found.")
+                return
+
+            # Compute Intervention Session Statistics
+            intervention_stats = data_processor.compute_intervention_statistics(df)
+            st.subheader("Intervention Session Statistics")
+            st.write(intervention_stats)
+
+            # Plot and download intervention statistics
+            intervention_fig = visualization.plot_intervention_statistics(intervention_stats)
+            visualization.download_chart(intervention_fig, "intervention_statistics_chart.png")
+
+            # Compute Student Metrics
+            student_metrics_df = data_processor.compute_student_metrics(df)
+            st.subheader("Student Metrics")
+            st.write(student_metrics_df)
+
+            # Compute Student Metric Averages
+            attendance_avg_stats, engagement_avg_stats = data_processor.compute_average_metrics(student_metrics_df)
+
+            # Plot and download student metrics
+            student_metrics_fig = visualization.plot_student_metrics(student_metrics_df, attendance_avg_stats, engagement_avg_stats)
+            visualization.download_chart(student_metrics_fig, "student_metrics_chart.png")
+
+            # Prepare input for the language model
+            llm_input = ai_analysis.prepare_llm_input(student_metrics_df)
+
+            # Generate Notes and Recommendations using Hugging Face LLM
+            with st.spinner("Generating AI analysis..."):
+                recommendations = ai_analysis.prompt_response_from_hf_llm(llm_input)
+
+            st.subheader("AI Analysis")
+            st.markdown(recommendations)
+
+            # Download AI output
+            ai_analysis.download_llm_output(recommendations, "llm_output.txt")
+
+        except Exception as e:
+            st.error(f"Error reading the file: {str(e)}")
+
+if __name__ == '__main__':
+    main()