import gradio as gr
import zipfile
import json
import pandas as pd
from tensorflow.keras.models import model_from_json
from collections import Counter

def extract_tm_info(tm_path):
    with zipfile.ZipFile(tm_path, 'r') as zip_ref:
        with zip_ref.open('manifest.json') as f:
            manifest = json.load(f)
        return {
            'type': manifest.get('type', 'N/A'),
            'version': manifest.get('version', 'N/A'),
            'epochs': manifest.get('appdata', {}).get('trainEpochs', 'N/A'),
            'batch_size': manifest.get('appdata', {}).get('trainBatchSize', 'N/A'),
            'learning_rate': manifest.get('appdata', {}).get('trainLearningRate', 'N/A')
        }

def extract_zip_info(zip_path):
    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        file_list = zip_ref.namelist()
        metadata = model_json = None
        weights_file = None

        for file in file_list:
            if 'metadata.json' in file:
                with zip_ref.open(file) as f:
                    metadata = json.load(f)
            elif 'model.json' in file:
                with zip_ref.open(file) as f:
                    model_json = json.load(f)
            elif 'model.weights.bin' in file:
                weights_file = file

        if model_json:
            model_topology_json = model_json['modelTopology']
            model_json_string = json.dumps(model_topology_json)
            model = model_from_json(model_json_string)
            summary = {'layer_counts': Counter()}
            extract_layer_info(model_topology_json['config']['layers'], summary)
            layer_counts_text = ', '.join([f'{k}: {v}' for k, v in summary['layer_counts'].items()])
        else:
            layer_counts_text = "Modelo não encontrado"

        weights_info = {'size_bytes': zip_ref.getinfo(weights_file).file_size} if weights_file else {'size_bytes': 'Não encontrado'}

        return {
            'metadata': metadata if metadata else 'Metadados não encontrados',
            'model_summary': layer_counts_text,
            'weights_info': weights_info
        }


def extract_layer_info(layers, summary):
    for layer in layers:
        class_name = layer['class_name']
        summary['layer_counts'][class_name] += 1
        if class_name in ['Sequential', 'Model']:
            sub_layers = layer['config']['layers']
            extract_layer_info(sub_layers, summary)

def analyze_files(tm_file, zip_file):
    results = {}
    if tm_file is not None:
        tm_info = extract_tm_info(tm_file.name)
        results['tm_info'] = tm_info
    if zip_file is not None:
        zip_info = extract_zip_info(zip_file.name)
        results['zip_info'] = zip_info
    return pd.DataFrame([results]).to_html(escape=False)

iface = gr.Interface(
    fn=analyze_files,
    inputs=[
        gr.File(label="Upload .tm File"),
        gr.File(label="Upload .zip File")
    ],
    outputs=gr.HTML(),
    title="GTM-Scope",
    description="Upload a .tm or .zip file to extract its information."
)

iface.launch(debug=True)