app.py

import streamlit as st
import pandas as pd
import json
import io
import math

# Function to load the .ifs file
def load_ifs_file(file):
    try:
        return json.load(file), None
    except Exception as e:
        return None, str(e)

# Function to extract values based on CODE_NEO paths
def extract_values(json_data, code_neo_list):
    extracted_values = []
    for code in code_neo_list:
        try:
            # Navigate to "data -> modules -> food_8 -> questions"
            value = json_data.get("data", {}).get("modules", {}).get("food_8", {}).get("questions", {})
            keys = code.replace("data_modules_food_8_questions_", "").split("_")
            for key in keys:
                if isinstance(value, list):  # Handle lists
                    value = value[0] if len(value) > 0 else None
                if isinstance(value, dict):
                    value = value.get(key, None)
            # Extract the 'answer' key if it's a dictionary
            if isinstance(value, dict):
                value = value.get("answer", None)
            extracted_values.append(value)
        except (KeyError, TypeError, IndexError):
            extracted_values.append(None)
    return extracted_values

# Function to safely update JSON with new values while preserving original data
def update_json_safe(json_data, code_neo_list, new_values):
    for code, new_value in zip(code_neo_list, new_values):
        try:
            # Skip if new_value is NaN or None to preserve original value
            if new_value is None or (isinstance(new_value, float) and math.isnan(new_value)):
                continue  
            
            # Navigate to the "data -> modules -> food_8 -> questions" level
            value = json_data.get("data", {}).get("modules", {}).get("food_8", {}).get("questions", {})
            keys = code.replace("data_modules_food_8_questions_", "").split("_")
            for key in keys[:-1]:  # Traverse to the second-to-last key
                if isinstance(value, list):
                    value = value[0] if len(value) > 0 else None
                if isinstance(value, dict):
                    value = value.get(key, None)
            # Update the 'answer' key if it exists
            if isinstance(value, dict):
                value["answer"] = new_value
        except (KeyError, TypeError, IndexError):
            pass
    return json_data

# Streamlit App
st.title("JSON to Excel Mapping and Update")

# Mode Selection
mode = st.sidebar.radio("Select Mode", ["Extraction Mode", "Update Mode"])

if mode == "Extraction Mode":
    st.header("Extraction Mode")
    
    # Upload .ifs file
    uploaded_json_file = st.file_uploader("Upload .ifs File", type=["ifs"])
    # Upload Excel template
    uploaded_excel_template = st.file_uploader("Upload Excel Template", type=["xls", "xlsx"])
    
    if uploaded_json_file and uploaded_excel_template:
        # Load JSON file
        json_data, json_error = load_ifs_file(uploaded_json_file)
        if json_error:
            st.error(f"Error loading JSON: {json_error}")
        else:
            # Load Excel template
            template_df = pd.read_excel(uploaded_excel_template, sheet_name="profil")
            code_neo_list = template_df["CODE_NEO"].tolist()
            
            # Extract data from JSON
            extracted_values = extract_values(json_data, code_neo_list)
            template_df["TEXTE_NEO"] = extracted_values
            
            # Create an Excel file for download
            output = io.BytesIO()
            with pd.ExcelWriter(output, engine="openpyxl") as writer:
                template_df.to_excel(writer, index=False, sheet_name="profil")
            output.seek(0)
            
            # Download button
            st.success("Data extracted successfully!")
            st.download_button(
                label="Download Extracted Excel",
                data=output,
                file_name="extracted_data.xlsx",
                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
            )

elif mode == "Update Mode":
    st.header("Update Mode")
    
    # Upload original .ifs file
    uploaded_json_file = st.file_uploader("Upload Original .ifs File", type=["ifs"])
    # Upload updated Excel file
    uploaded_updated_excel = st.file_uploader("Upload Updated Excel File", type=["xls", "xlsx"])
    
    if uploaded_json_file and uploaded_updated_excel:
        # Load JSON file
        json_data, json_error = load_ifs_file(uploaded_json_file)
        if json_error:
            st.error(f"Error loading JSON: {json_error}")
        else:
            # Load updated Excel file
            updated_df = pd.read_excel(uploaded_updated_excel, sheet_name="profil")
            code_neo_list = updated_df["CODE_NEO"].tolist()
            updated_values = updated_df["TEXTE_NEO"].tolist()
            
            # Update JSON
            updated_json_data = update_json_safe(json_data, code_neo_list, updated_values)
            
            # Provide updated JSON for download
            updated_json_str = json.dumps(updated_json_data, indent=4)
            st.success("JSON updated successfully!")
            st.download_button(
                label="Download Updated .ifs File",
                data=updated_json_str,
                file_name="updated_report.ifs",
                mime="application/json"
            )