app.py

import gradio as gr
import pandas as pd
from services.huggingface import init_huggingface, update_dataset
from services.json_generator import generate_json
from ui.form_components import (
    create_header_tab,
    create_task_tab,
    create_measures_tab,
    create_system_tab,
    create_software_tab,
    create_infrastructure_tab,
    create_environment_tab,
    create_quality_tab,
    create_hash_tab
)

# Initialize Hugging Face
init_huggingface()

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## ML-related Data Collection Form")
    gr.Markdown("Welcome to this Huggingface space that helps you fill in a form for monitoring the energy consumption of an AI model.")


    csv_upload = gr.File(label="Upload CSV", file_types=[".csv"])
    gr.Label("Please upload a CSV file with the data you want to analyze.")

    # Create form tabs
    header_components = create_header_tab()
    task_components = create_task_tab()
    measures_components = create_measures_tab()
    system_components = create_system_tab()
    software_components = create_software_tab()
    infrastructure_components = create_infrastructure_tab()
    environment_components = create_environment_tab()
    quality_components = create_quality_tab()
    hash_components = create_hash_tab()
    
    # Gather all form components in the order they appear in the inputs

    all_form_components = (
    header_components          # 11 items (indices 0-10)
    + task_components          # 28 items (indices 11-38)
    + measures_components      # 14 items (indices 39-52)
    + system_components        # 3 items (indices 53-55)
    + software_components      # 2 items (indices 56-57)
    + infrastructure_components # 10 items (indices 58-67)
    + environment_components   # 7 items (indices 68-74)
    + quality_components       # 1 item (index 75)
    + hash_components          # 3 items (indices 76-78)
)
    
    # Parse CSV and update form values
    def parse_csv_and_update_form(csv_file, *current_values):
        updated_values = list(current_values)
        if csv_file is None:
            return updated_values
        
        try:
            df = pd.read_csv(csv_file.name)
            csv_data = df.iloc[0].to_dict()
            
            # ========== HEADER ==========
            updated_values[3] = csv_data.get('run_id', '')  # reportId (index 3)
            updated_values[4] = csv_data.get('timestamp', '')  # reportDatetime (4)
            updated_values[8] = csv_data.get('project_name', '')  # publisher_projectName (8)
            
            # ========== SYSTEM ==========
            updated_values[54] = csv_data.get('os', '')  # os (index 53)
            updated_values[55] = ""  # distribution (54)
            updated_values[56] = ""  # distributionVersion (55)
            
            # ========== MEASURES ==========
            updated_values[40] = csv_data.get('tracking_mode', '')  # measurementMethod (39)
            updated_values[48] = "kWh"  # unit (47)
            updated_values[51] = csv_data.get('energy_consumed', '')  # powerConsumption (50)
            
            # Duration conversion (hours → seconds)
            if 'duration' in csv_data:
                try:
                    hours = float(csv_data['duration'])
                    updated_values[52] = str(round(hours * 3600, 2))  # measurementDuration (51)
                except:
                    updated_values[52] = ""
            
            updated_values[53] = csv_data.get('timestamp', '')  # measurementDateTime (52)
            
            # ========== SOFTWARE ==========
            updated_values[57] = "Python"  # language (56)
            updated_values[58] = csv_data.get('python_version', '')  # version_software (57)
            
            # ========== INFRASTRUCTURE ==========
            # infraType (58) - Dropdown
            on_cloud = str(csv_data.get('on_cloud', 'No')).lower().strip()
            updated_values[59] = "publicCloud" if on_cloud == "yes" else "onPremise"
            
            # Cloud fields (59-60)
            updated_values[59] = csv_data.get('cloud_provider', '') if on_cloud == "yes" else ""
            updated_values[60] = csv_data.get('cloud_region', '') if on_cloud == "yes" else ""
            
            # Component logic (61-67)
            gpu_count = int(csv_data.get('gpu_count', 0))
            cpu_count = int(csv_data.get('cpu_count', 0))
            
            if gpu_count > 0:
                
                updated_values[62] = str(gpu_count)  # nbComponent (62)
                model = csv_data.get('gpu_model', '')
            elif cpu_count > 0:
                u
                updated_values[62] = str(cpu_count)  # nbComponent (62)
                model = csv_data.get('cpu_model', '')
            else:
                model = ""
            
            # Memory size (63)
            ram_size = csv_data.get('ram_total_size', '')
            updated_values[63] = f"{ram_size} GB" if ram_size and float(ram_size) > 0 else ""
            
            # Split model into manufacturer/family/series (64-66)
            if model:
                parts = model.replace("(R)", "").replace("(TM)", "").split()
                updated_values[65] = parts[0] if parts else ""  # manufacturer_infra (64)
                updated_values[66] = " ".join(parts[1:3]) if len(parts) >= 3 else ""  # family (65)
                updated_values[67] = " ".join(parts[3:]) if len(parts) > 3 else ""  # series (66)
            else:
                updated_values[65] = updated_values[66] = updated_values[67] = ""
            
            updated_values[67] = ""  # share (67)
            
            # ========== ENVIRONMENT ==========
            updated_values[69] = csv_data.get('country_name', '')  # country (68)
            updated_values[70] = csv_data.get('latitude', '')  # latitude (69)
            updated_values[71] = csv_data.get('longitude', '')  # longitude (70)
            updated_values[72] = csv_data.get('region', '')  # location (71)
            
        except Exception as e:
            print(f"CSV Processing Error: {str(e)}")
        
        return updated_values

    # Parse CSV and update form values
    csv_upload.change(
        fn=parse_csv_and_update_form,
        inputs=[csv_upload] + all_form_components,
        outputs=all_form_components
    )

    # Submit and Download Buttons
    submit_button = gr.Button("Submit")
    output = gr.Textbox(label="Output", lines=1)
    json_output = gr.Textbox(visible=False)
    file_output = gr.File(label="Downloadable JSON")

    # Event Handlers
    submit_button.click(
        generate_json,
        inputs=[
            *header_components,
            *task_components,
            *measures_components,
            *system_components,
            *software_components,
            *infrastructure_components,
            *environment_components,
            *quality_components,
            *hash_components
        ],
        outputs=[output, file_output, json_output]
    ).then(
        update_dataset,
        inputs=json_output,
        outputs=output
    )

print(all_form_components)
print(len(all_form_components))

if __name__ == "__main__":
    demo.launch()
    print(all_form_components)