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import tempfile | |
import gradio as gr | |
from gtts import gTTS | |
import inference_script | |
import vit_gpt2 | |
import os | |
import warnings | |
warnings.filterwarnings('ignore') | |
def process_image_and_generate_output(image, model_selection): | |
if model_selection == ('Basic Model (Trained only for 15 epochs without any hyperparameter tuning, utilizing ' | |
'inception v3)'): | |
result = inference_script.evaluate(image) | |
pred_caption = ' '.join(result).rsplit(' ', 1)[0] | |
pred_caption = pred_caption.replace('<unk>', '') | |
elif model_selection == 'ViT-GPT2 (SOTA model for Image captioning)': | |
result = vit_gpt2.predict_step(image) | |
pred_caption = result[0] | |
else: | |
return "Invalid model selection", None | |
# Generate speech from the caption | |
tts = gTTS(text=pred_caption, lang='en', slow=False) | |
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_audio: | |
audio_file_path = temp_audio.name | |
tts.save(audio_file_path) | |
# Read the audio file | |
with open(audio_file_path, "rb") as f: | |
audio_content = f.read() | |
# Clean up the temporary audio file | |
os.unlink(audio_file_path) | |
return pred_caption, audio_content | |
iface = gr.Interface(fn=process_image_and_generate_output, | |
inputs=["image", gr.Radio(["Basic Model (Trained only for 15 epochs without any hyperparameter " | |
"tuning, utilizing inception v3)", "ViT-GPT2 (SOTA model for Image " | |
"captioning)"], label="Choose " | |
"Model")], | |
outputs=["text", "audio"], | |
title="Eye For Blind | Image Captioning & TTS", | |
description="To be added") | |
iface.launch() | |