Spaces:
Sleeping
Sleeping
File size: 2,616 Bytes
73d4923 3a2d1fe 73d4923 3a2d1fe 73d4923 3a2d1fe 73d4923 3a2d1fe 73d4923 3a2d1fe 73d4923 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
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
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
# Define your sample images
# sample_images = [os.path.join(os.path.dirname(__file__), 'sample_images/1.jpg'),
# os.path.join(os.path.dirname(__file__), 'sample_images/2.jpg'),
# os.path.join(os.path.dirname(__file__), 'sample_images/3.jpg'),
# os.path.join(os.path.dirname(__file__), 'sample_images/4.jpg'), ]
sample_images = [
["sample_images/1.jpg"],
["sample_images/2.jpg"],
["sample_images/3.jpg"],
["sample_images/4.jpg"]
]
# Create a dropdown to select sample image
image_input = gr.Image(label="Upload Image", sources=['upload', 'webcam'])
# Create a dropdown to choose the model
model_selection_input = 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")
iface = gr.Interface(fn=process_image_and_generate_output,
inputs=[image_input, model_selection_input],
outputs=["text", "audio"],
examples=sample_images,
title="Eye For Blind | Image Captioning & TTS",
description="To be added")
iface.launch()
|