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dca8200
1
Parent(s):
473dfd0
modify app.py
Browse files
app.py
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"Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.8/dist-packages (from Mako->alembic>=0.6.2->dataset) (2.0.1)\n",
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.8/dist-packages (0.1.97)\n",
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 723
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"id": "5DNOX2BIU5mO",
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"outputId": "9311e1ba-5685-4f74-e40a-0f1cd39484d0"
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"outputs": [
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"name": "stderr",
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"text": [
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"Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.6.0\n",
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"/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
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" warnings.warn(\n",
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"/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
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" warnings.warn(msg)\n"
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"text": [
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"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
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"Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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"output_type": "display_data",
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"<IPython.core.display.Javascript object>"
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"application/javascript": [
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"(async (port, path, width, height, cache, element) => {\n",
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" if (!google.colab.kernel.accessAllowed && !cache) {\n",
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" element.appendChild(document.createTextNode(''));\n",
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" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
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" const external_link = document.createElement('div');\n",
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" external_link.innerHTML = `\n",
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" <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
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" https://localhost:${port}${path}\n",
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" })(7882, \"/\", \"100%\", 500, false, window.element)"
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"metadata": {}
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"metadata": {},
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"execution_count": 64
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}
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],
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"source": [
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"import gradio as gr\n",
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"import requests\n",
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"from PIL import Image\n",
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"from torchvision import transforms\n",
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"from transformers import pipeline\n",
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"import torch\n",
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"import sentencepiece\n",
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"import re\n",
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"import googletrans \n",
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"from googletrans import Translator\n",
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"\n",
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"model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n",
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"\n",
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"from transformers import SegformerFeatureExtractor, SegformerForImageClassification, T5Tokenizer, T5Model\n",
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"from PIL import Image\n",
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"import requests\n",
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"\n",
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"def loadImageToText(image, argument):\n",
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" # url = \"https://media.istockphoto.com/id/470604022/es/foto/%C3%A1rbol-de-manzano.jpg?s=1024x1024&w=is&k=20&c=R7b6jPeTGsDw75Sqn3VwpNRckqlAkJNPLelb48pCk2U=\"\n",
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" # image = Image.open(requests.get(url, stream=True).raw)\n",
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" feature_extractor = SegformerFeatureExtractor.from_pretrained(\"nvidia/mit-b2\")\n",
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" model = SegformerForImageClassification.from_pretrained(\"nvidia/mit-b2\")\n",
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" translator = Translator()\n",
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"\n",
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" inputs = feature_extractor(images=image, return_tensors=\"pt\")\n",
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" outputs = model(**inputs)\n",
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" logits = outputs.logits\n",
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" # model predicts one of the 1000 ImageNet classes\n",
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" predicted_class_idx = logits.argmax(-1).item()\n",
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" part_args = f\"<\"+re.sub(\"[^(\\w|<|>)]+(?=\\w)\", \"><\", argument) + \">\"\n",
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" story_gen = pipeline(\"text-generation\", \"pranavpsv/gpt2-genre-story-generator\")\n",
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" story_text = story_gen(part_args + model.config.id2label[predicted_class_idx])\n",
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"\n",
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" generate_text_stroy = story_text[0][\"generated_text\"]\n",
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" ln_text_story = generate_text_stroy[len(part_args):len(generate_text_stroy)]\n",
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"\n",
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" translated_ita = translator.translate(ln_text_story, src='en', dest='es')\n",
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"\n",
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" return translated_ita.text\n",
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"\n",
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"\n",
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"\n",
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"#print(loadImageToText(\"animal,super\")) # borra el argumento 'image' y sus variables internas si quieres probarlo desde aquí.\n",
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"\n",
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"gr.Interface(fn=loadImageToText,\n",
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" inputs=[gr.Image(), gr.Text(label=\"Argumentos base\", placeholder=\"Verano, película,playa, superhéroe, animal\")],\n",
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" outputs=\"text\").launch()\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "zMmh7-ixXNIH"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}
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# -*- coding: utf-8 -*-
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"""imagetortext.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1UXh8tivt-4vHaBeXgfyq-TYLvpJAMZVV
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"""
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!pip install transformers dataset
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!pip install -q gradio
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!pip install sentencepiece
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!pip install googletrans==3.1.0a0
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import gradio as gr
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import requests
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from PIL import Image
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from torchvision import transforms
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from transformers import pipeline
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import torch
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import sentencepiece
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import re
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import googletrans
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from googletrans import Translator
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model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
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from transformers import SegformerFeatureExtractor, SegformerForImageClassification, T5Tokenizer, T5Model
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from PIL import Image
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import requests
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def loadImageToText(image, argument):
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# url = "https://media.istockphoto.com/id/470604022/es/foto/%C3%A1rbol-de-manzano.jpg?s=1024x1024&w=is&k=20&c=R7b6jPeTGsDw75Sqn3VwpNRckqlAkJNPLelb48pCk2U="
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# image = Image.open(requests.get(url, stream=True).raw)
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feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/mit-b2")
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model = SegformerForImageClassification.from_pretrained("nvidia/mit-b2")
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translator = Translator()
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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# model predicts one of the 1000 ImageNet classes
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predicted_class_idx = logits.argmax(-1).item()
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part_args = f"<"+re.sub("[^(\w|<|>)]+(?=\w)", "><", argument) + ">"
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story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator")
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story_text = story_gen(part_args + model.config.id2label[predicted_class_idx])
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generate_text_stroy = story_text[0]["generated_text"]
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ln_text_story = generate_text_stroy[len(part_args):len(generate_text_stroy)]
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translated_ita = translator.translate(ln_text_story, src='en', dest='es')
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return translated_ita.text
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#print(loadImageToText("animal,super")) # borra el argumento 'image' y sus variables internas si quieres probarlo desde aquí.
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gr.Interface(fn=loadImageToText,
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inputs=[gr.Image(), gr.Text(label="Argumentos base", placeholder="Verano, película,playa, superhéroe, animal")],
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outputs="text").launch()
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