Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from st_utils import ( | |
| load_tokenizer_and_model, | |
| generate_docstring, | |
| download_model, | |
| # list_files, | |
| ) | |
| from huggingface_hub import hf_hub_download | |
| import os | |
| import torch | |
| # list_files(os.getcwd()) | |
| # Set the title and description of the app | |
| st.title("Text Summarization App") | |
| st.write( | |
| """ | |
| This app uses the Hugging Face transformers library to generate summaries of input text. | |
| Simply select one of the sample Python functions from the dropdown menu below, and click the 'Summarize' button to generate a summary. | |
| """ | |
| ) | |
| st.write(f"Has CUDA: {torch.cuda.is_available()}") | |
| # Download the model from the Hugging Face Hub if it doesn't exist | |
| download_model() | |
| # load the tokenizer and model | |
| tokenizer, model, device = load_tokenizer_and_model("./models/pytorch_model.bin") | |
| # Create a dropdown menu for the user to select a sample Python function | |
| values = [ | |
| "", | |
| "def multiply(a, b):\n return a * b", | |
| "def get_data():\n data = []\n for i in range(10):\n data.append(i)\n return data", | |
| "def search(data, target):\n for i in range(len(data)):\n if data[i] == target:\n return i\n return -1", | |
| ] | |
| st.subheader("Select a sample Python function:") | |
| selected_value = st.selectbox("", values) | |
| # Create a text input area for the user to enter their text | |
| text_input = st.text_area( | |
| "Or enter your Python function here:", | |
| height=300, | |
| value=values[0], | |
| ) | |
| # Define a function to generate a summary | |
| def generate_summary(text): | |
| summary = generate_docstring(model, tokenizer, device, text, max_length=30) | |
| return summary | |
| # When the user clicks the 'Summarize' button, generate a summary | |
| if st.button("Summarize") and (len(selected_value) > 0 or len(text_input) > 0): | |
| with st.spinner("Generating summary..."): | |
| if len(selected_value) > 0: | |
| summaries = generate_summary(selected_value) | |
| st.subheader("Docstrings:") | |
| for i, summary in enumerate(summaries): | |
| st.write(f"{i + 1}. " + summary) | |
| else: | |
| summaries = generate_summary(text_input) | |
| st.subheader("Docstrings:") | |
| for i, summary in enumerate(summaries): | |
| st.write(f"{i + 1}. " + summary) | |
| # import streamlit as st | |
| # from st_utils import load_tokenizer_and_model, generate_docstring, download_model | |
| # # Download the model from the Hugging Face Hub if it doesn't exist | |
| # # Set the title and description of the app | |
| # st.title("Text Summarization App") | |
| # st.write( | |
| # """ | |
| # This app uses the Hugging Face transformers library to generate summaries of input text. | |
| # Simply enter your text in the input area below, and click the 'Summarize' button to generate a summary. | |
| # """ | |
| # ) | |
| # tokenizer, model, device = load_tokenizer_and_model("./models/pytorch_model.bin") | |
| # # Create a text input area for the user to enter their text | |
| # values = [ | |
| # "def multiply(a, b):\n return a * b", | |
| # "def get_data():\n data = []\n for i in range(10):\n data.append(i)\n return data", | |
| # "def search(data, target):\n for i in range(len(data)):\n if data[i] == target:\n return i\n return -1", | |
| # ] | |
| # st.subheader("Enter your Python function here:") | |
| # text_input = st.text_area( | |
| # "Input text here...", | |
| # height=300, | |
| # value=values[2], | |
| # ) | |
| # # Define a function to generate a summary | |
| # def generate_summary(text): | |
| # summary = generate_docstring(model, tokenizer, device, text, max_length=30) | |
| # return summary | |
| # # When the user clicks the 'Summarize' button, generate a summary | |
| # if st.button("Summarize") and len(text_input) > 0: | |
| # with st.spinner("Generating summary..."): | |
| # # summary = generate_summary(text_input) | |
| # # st.write("Summary:") | |
| # # st.code(summary, language="text") | |
| # summaries = generate_summary(text_input) | |
| # st.subheader("Summary:") | |
| # for i, summary in enumerate(summaries): | |
| # st.write(f"{i + 1}. " + summary) | |