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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_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)
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