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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 image is None:
return "Please select an image", None
# (Trained only for 15 epochs without any hyperparameter tuning, utilizing inception v3)'
# (SOTA model for Image captioning)
if model_selection == ('Basic Model'):
result = inference_script.evaluate(image)
pred_caption = ' '.join(result).rsplit(' ', 1)[0]
pred_caption = pred_caption.replace('<unk>', '')
elif model_selection == 'ViT-GPT2':
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
# sample_images = [
# [os.path.join(os.path.dirname(__file__), "sample_images/1.jpg"), 'Basic Model'],
# [os.path.join(os.path.dirname(__file__), "sample_images/2.jpg"), 'Basic Model'],
# [os.path.join(os.path.dirname(__file__), "sample_images/3.jpg"), 'Basic Model'],
# [os.path.join(os.path.dirname(__file__), "sample_images/4.jpg"), "ViT-GPT2"],
# [os.path.join(os.path.dirname(__file__), "sample_images/5.jpg"), "ViT-GPT2"],
# [os.path.join(os.path.dirname(__file__), "sample_images/6.jpg"), "ViT-GPT2"]
# ]
sample_images = [
["sample_images/1.jpg"],
["sample_images/2.jpg"],
["sample_images/3.jpg"],
["sample_images/4.jpg"],
["sample_images/5.jpg"],
["sample_images/6.jpg"]
]
# Create a dropdown to select sample image
image_input = gr.Image(label="Upload Image")
# Create a dropdown to choose the model
model_selection_input = gr.Radio(["Basic Model",
"ViT-GPT2"],
label="Choose Model")
iface = gr.Interface(fn=process_image_and_generate_output,
inputs=[image_input, model_selection_input],
outputs=["text", "audio"],
examples=sample_images,
cache_examples=False,
allow_flagging='never',
title="Eye For Blind | Image Captioning & TTS",
description="To be added")
iface.launch()
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