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import gradio as gr |
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import os |
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import time |
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import shutil |
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import base64 |
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from pdfquery import PDFQuery |
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pquery = PDFQuery() |
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def openai_create(s): |
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global pquery |
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return pquery.ask(s) |
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def chatgpt_clone(input, history, chatbot): |
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if input == "": |
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return chatbot, history, "" |
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history = history or [] |
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s = list(sum(history, ())) |
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s.append(input) |
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inp = ' '.join(s) |
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output = openai_create(input) |
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history.append((inp, output)) |
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chatbot.append((input, output)) |
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return chatbot, history, "" |
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title_html = f"<h1 align=\"center\">Chat With Pdf</h1>" |
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gr_L1 = lambda: gr.Row().style() |
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gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id) |
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def pdf_to_markdown(file_obj): |
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try: |
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shutil.rmtree('./private_upload/') |
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except: |
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pass |
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time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) |
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os.makedirs(f'private_upload/{time_tag}', exist_ok=True) |
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file_name = os.path.basename(file_obj.name) |
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destination = f'private_upload/{time_tag}/{file_name}' |
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shutil.copy(file_obj.name, destination) |
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global pquery |
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pquery.ingest(destination) |
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with open(destination, "rb") as f: |
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pdf = base64.b64encode(f.read()).decode('utf-8') |
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pdf_display = f'<embed src="data:application/pdf;base64,{pdf}" ' \ |
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f'width="700" height="800" type="application/pdf">' |
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return [pdf_display, gr.update(visible=False),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True), |
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gr.update(visible=True),gr.update(visible=True)] |
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cle = lambda :"" |
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with gr.Blocks(title="Chat With Pdf") as demo: |
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gr.HTML(title_html) |
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file = gr.File() |
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with gr_L1(): |
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with gr_L2(scale=1.5, elem_id="gpt-chat"): |
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out = gr.Markdown() |
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with gr_L2(scale=1, elem_id="gpt-chat"): |
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title = gr.Markdown("""<h1><center><strong>文档问答 </strong></center></h1> |
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""", visible=False) |
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chatbot = gr.Chatbot(scale=3, height=600, visible=False) |
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with gr_L1(): |
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message = gr.Textbox(placeholder="Input question here.", scale=10, visible=False) |
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state = gr.State([]) |
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submit = gr.Button("发送", scale=1, visible=False) |
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file.upload(pdf_to_markdown, file, [out, file, out, title, chatbot, message, submit]) |
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submit.click(chatgpt_clone, inputs=[message, state, chatbot], outputs=[chatbot, state, message]) |
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demo.launch() |
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