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)