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requirements.txt

@@ -1,4 +1,4 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@4ba7b238e22ac042de14a6b69aa9d61536ddffba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/4ba7b238e22ac042de14a6b69aa9d61536ddffba/gradio-4.40.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@890bae3942cc19f2b9040cfb6792adaa3cd478b0#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/890bae3942cc19f2b9040cfb6792adaa3cd478b0/gradio-4.40.0-py3-none-any.whl
 torch
 transformers

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: unified_demo_text_generation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "\n", "generator = pipeline('text-generation', model = 'gpt2')\n", "\n", "def generate_text(text_prompt):\n", "  response = generator(text_prompt, max_length = 30, num_return_sequences=5)\n", "  return response[0]['generated_text']  #type: ignore\n", "\n", "textbox = gr.Textbox()\n", "\n", "demo = gr.Interface(generate_text, textbox, textbox)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: unified_demo_text_generation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "\n", "generator = pipeline('text-generation', model = 'gpt2')\n", "\n", "def generate_text(text_prompt):\n", "  response = generator(text_prompt, max_length = 30, num_return_sequences=5)\n", "  return response[0]['generated_text']  # type: ignore\n", "\n", "textbox = gr.Textbox()\n", "\n", "demo = gr.Interface(generate_text, textbox, textbox)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -5,7 +5,7 @@ generator = pipeline('text-generation', model = 'gpt2')
 
 def generate_text(text_prompt):
   response = generator(text_prompt, max_length = 30, num_return_sequences=5)
-  return response[0]['generated_text']  #type: ignore
+  return response[0]['generated_text']  # type: ignore
 
 textbox = gr.Textbox()