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import os |
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from PIL import Image, ImageDraw, ImageFont |
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import gradio as gr |
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from helper import load_image_from_url, render_results_in_image |
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from helper import summarize_predictions_natural_language |
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from transformers import pipeline |
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from transformers.utils import logging |
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logging.set_verbosity_error() |
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from helper import ignore_warnings |
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ignore_warnings() |
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od_pipe = pipeline("object-detection", "facebook/detr-resnet-50") |
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tts_pipe = pipeline("text-to-speech", |
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model="kakao-enterprise/vits-ljs") |
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def get_pipeline_prediction(pil_image): |
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pipeline_output = od_pipe(pil_image) |
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processed_image = render_results_in_image(pil_image, |
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pipeline_output) |
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text = summarize_predictions_natural_language(pipeline_output) |
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print(text) |
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narrated_text = tts_pipe(text) |
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print(narrated_text["audio"][0]) |
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print (narrated_text["sampling_rate"]) |
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return processed_image, (narrated_text["sampling_rate"], narrated_text["audio"][0] ) |
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demo = gr.Interface( |
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fn=get_pipeline_prediction, |
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inputs=gr.Image(label="Input image", |
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type="pil"), |
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outputs=[gr.Image(label="Output image with predicted instances", |
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type="pil"), gr.Audio(label="Narration", type="numpy", autoplay=True)] |
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) |
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demo.launch(server_name="0.0.0.0", server_port=7860) |
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