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bhaskartripathi
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241e0dd
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Parent(s):
957bf1b
Update app.py
Browse files
app.py
CHANGED
@@ -206,13 +206,22 @@ def generate_answer_text_davinci_003(question,openAI_key):
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answer = generate_text_text_davinci_003(openAI_key, prompt,"text-davinci-003")
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return answer
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recommender = SemanticSearch()
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title = 'PDF GPT Turbo'
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description = """ PDF GPT Turbo 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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-
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with gr.Blocks() as demo:
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gr.Markdown(f'<center><h1>{title}</h1></center>')
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@@ -228,6 +237,12 @@ with gr.Blocks() as demo:
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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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question = gr.Textbox(label='Enter your question here')
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model = gr.Radio(['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'text-davinci-003','gpt-4','gpt-4-32k'], label='Select Model', default='gpt-3.5-turbo')
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#model = gr.Dropdown(choices=['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'text-davinci-003'], label='Select Large Language Model', default='gpt-3.5-turbo')
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btn = gr.Button(value='Submit')
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answer = generate_text_text_davinci_003(openAI_key, prompt,"text-davinci-003")
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return answer
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# pre-defined questions
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questions = [
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"what did the study be-60",
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"what are the methods used in this study?",
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"what are the methodologies used in this study?",
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"what are the main findings of the study?",
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"what are the main results of the study?",
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"what is the contribution of this study?",
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"what is the conclusion of this study?",
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]
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recommender = SemanticSearch()
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title = 'PDF GPT Turbo'
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description = """ PDF GPT Turbo 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(f'<center><h1>{title}</h1></center>')
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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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question = gr.Textbox(label='Enter your question here')
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#Predefined questions
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gr.Examples(
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[[q] for q in questions],
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inputs=[question],
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label="Click on any example and press Enter in the input textbox!",
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)
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model = gr.Radio(['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'text-davinci-003','gpt-4','gpt-4-32k'], label='Select Model', default='gpt-3.5-turbo')
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#model = gr.Dropdown(choices=['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'text-davinci-003'], label='Select Large Language Model', default='gpt-3.5-turbo')
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btn = gr.Button(value='Submit')
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