bhaskartripathi commited on
Commit
ced6c28
·
1 Parent(s): ed754c5

Update app.py

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Files changed (1) hide show
  1. app.py +13 -7
app.py CHANGED
@@ -7,6 +7,8 @@ import openai
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  import gradio as gr
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  import os
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  from sklearn.neighbors import NearestNeighbors
 
 
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  def download_pdf(url, output_path):
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  urllib.request.urlretrieve(url, output_path)
@@ -155,14 +157,18 @@ def question_answer(url, file, question,openAI_key):
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  if question.strip() == '':
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  return '[ERROR]: Question field is empty'
 
 
 
 
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- return generate_answer(question,openAI_key)
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  recommender = SemanticSearch()
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  title = 'PDF GPT'
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- description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
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  with gr.Blocks() as demo:
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@@ -170,10 +176,10 @@ with gr.Blocks() as demo:
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  gr.Markdown(description)
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  with gr.Row():
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-
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  with gr.Group():
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  gr.Markdown(f'<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>')
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- openAI_key=gr.Textbox(label='Enter your OpenAI API key here')
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  url = gr.Textbox(label='Enter PDF URL here')
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  gr.Markdown("<center><h4>OR<h4></center>")
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  file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
@@ -184,6 +190,6 @@ with gr.Blocks() as demo:
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  with gr.Group():
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  answer = gr.Textbox(label='The answer to your question is :')
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- btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
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- #openai.api_key = os.getenv('Your_Key_Here')
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- demo.launch()
 
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  import gradio as gr
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  import os
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  from sklearn.neighbors import NearestNeighbors
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+ from gradio.mix import Interface
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+
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  def download_pdf(url, output_path):
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  urllib.request.urlretrieve(url, output_path)
 
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  if question.strip() == '':
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  return '[ERROR]: Question field is empty'
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+
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+ answer = generate_answer(question,openAI_key)
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+ # Convert answer to HTML with clickable citations
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+ answer_html = re.sub(r'\[(\d+)\]', r'<a href="#" onclick="navigateToPage(\1)">[\1]</a>', answer)
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+ return answer_html
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  recommender = SemanticSearch()
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  title = 'PDF GPT'
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+ description = """PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination-free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
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  with gr.Blocks() as demo:
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  gr.Markdown(description)
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  with gr.Row():
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+
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  with gr.Group():
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  gr.Markdown(f'<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>')
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+ openAI_key = gr.Textbox(label='Enter your OpenAI API key here')
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  url = gr.Textbox(label='Enter PDF URL here')
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  gr.Markdown("<center><h4>OR<h4></center>")
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  file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
 
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  with gr.Group():
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  answer = gr.Textbox(label='The answer to your question is :')
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+ btn.click(question_answer, inputs=[url, file, question, openAI_key], outputs=[answer])
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+
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+ demo.launch()