bilgeyucel commited on
Commit
3f89845
1 Parent(s): 3de0eaa

Imorove ui

Browse files
Files changed (2) hide show
  1. app.py +11 -6
  2. utils.py +1 -2
app.py CHANGED
@@ -8,12 +8,13 @@ def run_prompt(prompt, api_key, model_name, max_length):
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  lemmatized_prompt = lemmatizer_func(prompt)
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  response_plain = prompt_node(prompt)
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  response_lemmatized = prompt_node(lemmatized_prompt)
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- return response_plain[0][0], response_plain[1]["total_tokens"], response_lemmatized[0][0], response_lemmatized[1]["total_tokens"]
12
 
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  with gr.Blocks() as demo:
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  with gr.Row():
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  api_key = gr.Textbox(label="Enter your api key")
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  model_name = gr.Dropdown(["text-davinci-003", "gpt-3.5-turbo", "gpt-4", "gpt-4-32k", "command", "command-light", "base", "base-light"], value="gpt-3.5-turbo", label="Choose your model!")
 
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  with gr.Row():
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  prompt = gr.TextArea(label="Prompt", value="Rachel has 17 apples. She gives 9 to Sarah. How many apples does Rachel have now?")
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  gr.Examples(
@@ -26,16 +27,20 @@ with gr.Blocks() as demo:
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  ],
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  inputs=prompt,
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  label="Click on any example and press Enter in the input textbox!",
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- )
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- max_length = gr.Slider(100, 500, value=100, step=10, label="Max Length", info="Choose between 100 and 500")
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  submit_btn = gr.Button("Submit")
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  with gr.Row():
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  prompt_response = gr.TextArea(label="Answer", show_copy_button=True)
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- token_count_plain = gr.Number(label="Plain Text Token Count")
 
 
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  with gr.Row():
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  lemmatized_prompt_response = gr.TextArea(label="Lemmatized Answer", show_copy_button=True)
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- token_count_lemmatized = gr.Number(label="Lemmatized Text Token Count")
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- submit_btn.click(fn=run_prompt, inputs=[prompt, api_key, model_name, max_length], outputs=[prompt_response, token_count_plain, lemmatized_prompt_response, token_count_lemmatized])
 
 
 
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  demo.launch()
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  lemmatized_prompt = lemmatizer_func(prompt)
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  response_plain = prompt_node(prompt)
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  response_lemmatized = prompt_node(lemmatized_prompt)
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+ return lemmatized_prompt, response_plain[0][0], response_plain[1]["prompt_tokens"], response_plain[1]["completion_tokens"], response_lemmatized[0][0], response_lemmatized[1]["prompt_tokens"], response_lemmatized[1]["completion_tokens"]
12
 
13
  with gr.Blocks() as demo:
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  with gr.Row():
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  api_key = gr.Textbox(label="Enter your api key")
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  model_name = gr.Dropdown(["text-davinci-003", "gpt-3.5-turbo", "gpt-4", "gpt-4-32k", "command", "command-light", "base", "base-light"], value="gpt-3.5-turbo", label="Choose your model!")
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+ max_length = gr.Slider(100, 500, value=100, step=10, label="Max Length", info="Max token length of the response. Choose between 100 and 500")
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  with gr.Row():
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  prompt = gr.TextArea(label="Prompt", value="Rachel has 17 apples. She gives 9 to Sarah. How many apples does Rachel have now?")
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  gr.Examples(
 
27
  ],
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  inputs=prompt,
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  label="Click on any example and press Enter in the input textbox!",
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+ )
 
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  submit_btn = gr.Button("Submit")
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  with gr.Row():
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  prompt_response = gr.TextArea(label="Answer", show_copy_button=True)
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+ with gr.Column():
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+ token_count_plain = gr.Number(label="Prompt Token Count")
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+ token_count_plain_completion = gr.Number(label="Completion Token Count")
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  with gr.Row():
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  lemmatized_prompt_response = gr.TextArea(label="Lemmatized Answer", show_copy_button=True)
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+ with gr.Column():
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+ lemmatized_prompt = gr.TextArea(label="Lemmatized Promp", show_copy_button=True)
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+ token_count_lemmatized = gr.Number(label="Lemmatized Text Token Count")
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+ token_count_lemmatized_completion = gr.Number(label="Completion Lemmatized Token Count")
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+ submit_btn.click(fn=run_prompt, inputs=[prompt, api_key, model_name, max_length], outputs=[lemmatized_prompt, prompt_response, token_count_plain, token_count_plain_completion, lemmatized_prompt_response, token_count_lemmatized, token_count_lemmatized_completion])
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  demo.launch()
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utils.py CHANGED
@@ -1,6 +1,5 @@
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  from simplemma import text_lemmatizer
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  def lemmatizer_func(plain_text):
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- words = text_lemmatizer(plain_text, lang="en")
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- lemmatized_promt = ' '.join(words)
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  return lemmatized_promt
 
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  from simplemma import text_lemmatizer
2
 
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  def lemmatizer_func(plain_text):
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+ lemmatized_promt = ' '.join([el for el in text_lemmatizer(plain_text, lang='en') if el.isalnum()])
 
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  return lemmatized_promt