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Browse files- app (7).py +209 -0
- requirements (7).txt +6 -0
app (7).py
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import gradio as gr
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import os
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import json
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import requests
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#Streaming endpoint
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API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
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OPENAI_API_KEY= os.environ["HF_TOKEN"] # Add a token to this space . Then copy it to the repository secret in this spaces settings panel. os.environ reads from there.
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# Keys for Open AI ChatGPT API usage are created from here: https://platform.openai.com/account/api-keys
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def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k
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# 1. Set up a payload
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payload = {
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": f"{inputs}"}],
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"temperature" : 1.0,
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"top_p":1.0,
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"n" : 1,
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"stream": True,
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"presence_penalty":0,
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"frequency_penalty":0,
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}
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# 2. Define your headers and add a key from https://platform.openai.com/account/api-keys
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {OPENAI_API_KEY}"
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}
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# 3. Create a chat counter loop that feeds [Predict next best anything based on last input and attention with memory defined by introspective attention over time]
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print(f"chat_counter - {chat_counter}")
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if chat_counter != 0 :
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messages=[]
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for data in chatbot:
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temp1 = {}
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temp1["role"] = "user"
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temp1["content"] = data[0]
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temp2 = {}
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temp2["role"] = "assistant"
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temp2["content"] = data[1]
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messages.append(temp1)
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messages.append(temp2)
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temp3 = {}
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temp3["role"] = "user"
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temp3["content"] = inputs
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messages.append(temp3)
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payload = {
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"model": "gpt-3.5-turbo",
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"messages": messages, #[{"role": "user", "content": f"{inputs}"}],
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"temperature" : temperature, #1.0,
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"top_p": top_p, #1.0,
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"n" : 1,
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"stream": True,
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"presence_penalty":0,
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"frequency_penalty":0,
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}
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chat_counter+=1
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# 4. POST it to OPENAI API
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history.append(inputs)
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print(f"payload is - {payload}")
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response = requests.post(API_URL, headers=headers, json=payload, stream=True)
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token_counter = 0
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partial_words = ""
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# 5. Iterate through response lines and structure readable response
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counter=0
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for chunk in response.iter_lines():
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if counter == 0:
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counter+=1
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continue
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if chunk.decode() :
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chunk = chunk.decode()
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if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
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partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
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if token_counter == 0:
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history.append(" " + partial_words)
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else:
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history[-1] = partial_words
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chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
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token_counter+=1
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yield chat, history, chat_counter
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def reset_textbox():
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return gr.update(value='')
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# Episodic and Semantic IO
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def list_files(file_path):
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import os
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icon_csv = "📄 "
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icon_txt = "📑 "
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current_directory = os.getcwd()
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file_list = []
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for filename in os.listdir(current_directory):
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if filename.endswith(".csv"):
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file_list.append(icon_csv + filename)
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elif filename.endswith(".txt"):
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file_list.append(icon_txt + filename)
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if file_list:
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return "\n".join(file_list)
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else:
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return "No .csv or .txt files found in the current directory."
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# Function to read a file
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def read_file(file_path):
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try:
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with open(file_path, "r") as file:
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contents = file.read()
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return f"{contents}"
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#return f"Contents of {file_path}:\n{contents}"
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except FileNotFoundError:
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return "File not found."
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# Function to delete a file
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def delete_file(file_path):
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try:
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import os
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os.remove(file_path)
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return f"{file_path} has been deleted."
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except FileNotFoundError:
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return "File not found."
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# Function to write to a file
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def write_file(file_path, content):
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try:
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with open(file_path, "w") as file:
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file.write(content)
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return f"Successfully written to {file_path}."
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except:
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return "Error occurred while writing to file."
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# Function to append to a file
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def append_file(file_path, content):
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try:
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with open(file_path, "a") as file:
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file.write(content)
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return f"Successfully appended to {file_path}."
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except:
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return "Error occurred while appending to file."
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title = """<h1 align="center">Memory Chat Story Generator ChatGPT</h1>"""
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description = """
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## ChatGPT Datasets 📚
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- WebText
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- Common Crawl
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- BooksCorpus
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- English Wikipedia
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- Toronto Books Corpus
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- OpenWebText
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## ChatGPT Datasets - Details 📚
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- **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2.
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- [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext)
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- **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3.
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- [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al.
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- **BooksCorpus:** A dataset of over 11,000 books from a variety of genres.
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- [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al.
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- **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017.
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- [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search
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- **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto.
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- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze.
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- **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3.
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- [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.
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"""
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# 6. Use Gradio to pull it all together
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with gr.Blocks(css = """#col_container {width: 1400px; margin-left: auto; margin-right: auto;} #chatbot {height: 600px; overflow: auto;}""") as demo:
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gr.HTML(title)
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with gr.Column(elem_id = "col_container"):
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inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
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chatbot = gr.Chatbot(elem_id='chatbot')
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state = gr.State([])
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b1 = gr.Button()
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with gr.Accordion("Parameters", open=False):
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top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
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chat_counter = gr.Number(value=0, visible=True, precision=0)
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# Episodic/Semantic IO
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fileName = gr.Textbox(label="Filename")
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fileContent = gr.TextArea(label="File Content")
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completedMessage = gr.Textbox(label="Completed")
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label = gr.Label()
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with gr.Row():
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listFiles = gr.Button("📄 List File(s)")
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readFile = gr.Button("📖 Read File")
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saveFile = gr.Button("💾 Save File")
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deleteFile = gr.Button("🗑️ Delete File")
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appendFile = gr.Button("➕ Append File")
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listFiles.click(list_files, inputs=fileName, outputs=fileContent)
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readFile.click(read_file, inputs=fileName, outputs=fileContent)
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saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage)
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deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage)
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appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage )
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inputs.submit(predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter])
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b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter])
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b1.click(reset_textbox, [], [inputs])
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inputs.submit(reset_textbox, [], [inputs])
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gr.Markdown(description)
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demo.queue().launch(debug=True)
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requirements (7).txt
ADDED
@@ -0,0 +1,6 @@
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transformers
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torch
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Werkzeug
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huggingface_hub
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Pillow
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datasets
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