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| import gradio as gr | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing import image | |
| import tensorflow as tf | |
| from PIL import Image | |
| import os | |
| model = load_model('model.h5') | |
| def convertToImage(file): | |
| with open(file, "rb") as f: | |
| byte = f.read(1) # reading 1 byte at a time. returns a byte object. | |
| i, j, p = 0, 0, 0 | |
| image = np.zeros((256, 256)) | |
| temp1 = None # Define temp1 before using it | |
| while byte: | |
| try: | |
| decoded_byte = byte.decode("utf-8") | |
| if ( | |
| decoded_byte == " " | |
| or decoded_byte == "\r" | |
| or decoded_byte == "\n" | |
| or decoded_byte == "?" | |
| ): | |
| byte = f.read(1) | |
| continue | |
| if j > 8: | |
| try: | |
| image[i][p] = int(decoded_byte, 16) | |
| p += 1 | |
| except ValueError: | |
| pass | |
| j += 1 | |
| if p > 255: | |
| p = 0 | |
| j = 0 | |
| if i < 255: | |
| i += 1 | |
| continue | |
| byte = f.read(1) | |
| except UnicodeDecodeError: | |
| byte = f.read(1) | |
| continue | |
| a = np.matrix(image) | |
| temp1 = a | |
| img = Image.fromarray(image, "L") | |
| img.save("a.bmp") | |
| def predict_gender(img): | |
| pathe = os.path.splitext(img.name) | |
| extension = pathe[1] | |
| filename = pathe[0] + ".bmp" | |
| if(not extension == ".bmp"): | |
| convertToImage(img.name) | |
| filename = "a.bmp" | |
| img = image.load_img(filename, target_size=(256, 256)) | |
| x = image.img_to_array(img) | |
| x = np.expand_dims(x, axis=0) | |
| x = x / 255.0 | |
| preds = model.predict(x) | |
| if preds[0][0] > 0.5: | |
| return "Predicted malware" | |
| else: | |
| return "Predicted benign" | |
| # Create an interface with a file input and a text output | |
| interface = gr.Interface( | |
| fn=predict_gender, | |
| inputs=gr.inputs.File(label="Upload a file"), | |
| outputs="text" | |
| ) | |
| # Launch the interface | |
| interface.launch() |