manandey commited on
Commit
e0a6493
1 Parent(s): d03a1e0

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +5 -4
app.py CHANGED
@@ -7,7 +7,7 @@ import gradio as gr
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- def generate(html, entity, website_desc, datasource, year, month, title):
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  html_text = "html | " if html == "on" else ""
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  entity_text = ""
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  if entity != "":
@@ -23,12 +23,12 @@ def generate(html, entity, website_desc, datasource, year, month, title):
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  month_text = "Month: " + month + " | " if month != "" else ""
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  title_text = "Title: " + title + " | " if title != "" else ""
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- prompt = html_text + year_text + month_text + website_desc_text + title_text + datasource_text + entity_text
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  model = AutoModelForCausalLM.from_pretrained("bs-modeling-metadata/checkpoints_all_04_23", subfolder="checkpoint-30000step")
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  tokenizer = AutoTokenizer.from_pretrained("bs-modeling-metadata/checkpoints_all_04_23", subfolder="tokenizer")
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- inputs = tokenizer(prompt, return_tensors="pt")
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  outputs = model.generate(**inputs, max_new_tokens=128)
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  return tokenizer.batch_decode(outputs, skip_special_tokens=True)
@@ -41,10 +41,11 @@ datasource = gr.Textbox(placeholder="enter a datasource", label="datasource")
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  year = gr.Textbox(placeholder="enter a year", label="year")
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  month = gr.Textbox(placeholder="enter a month", label="month")
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  title = gr.Textbox(placeholder="enter a website title", label="website title")
 
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  demo = gr.Interface(
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  fn=generate,
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- inputs=[html, entity, website_desc, datasource, year, month, title],
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  outputs="text",
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  )
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  demo.launch()
 
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ def generate(html, entity, website_desc, datasource, year, month, title, prompt):
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  html_text = "html | " if html == "on" else ""
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  entity_text = ""
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  if entity != "":
 
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  month_text = "Month: " + month + " | " if month != "" else ""
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  title_text = "Title: " + title + " | " if title != "" else ""
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+ final_prompt = html_text + year_text + month_text + website_desc_text + title_text + datasource_text + entity_text + prompt
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  model = AutoModelForCausalLM.from_pretrained("bs-modeling-metadata/checkpoints_all_04_23", subfolder="checkpoint-30000step")
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  tokenizer = AutoTokenizer.from_pretrained("bs-modeling-metadata/checkpoints_all_04_23", subfolder="tokenizer")
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+ inputs = tokenizer(final_prompt, return_tensors="pt")
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  outputs = model.generate(**inputs, max_new_tokens=128)
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  return tokenizer.batch_decode(outputs, skip_special_tokens=True)
 
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  year = gr.Textbox(placeholder="enter a year", label="year")
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  month = gr.Textbox(placeholder="enter a month", label="month")
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  title = gr.Textbox(placeholder="enter a website title", label="website title")
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+ prompt = gr.Textbox(placeholder="enter a prompt", label="prompt")
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  demo = gr.Interface(
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  fn=generate,
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+ inputs=[html, entity, website_desc, datasource, year, month, title, prompt],
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  outputs="text",
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  )
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  demo.launch()