hunterXdk commited on
Commit
dc294a4
1 Parent(s): 96501a7
Files changed (1) hide show
  1. app.py +6 -6
app.py CHANGED
@@ -93,7 +93,7 @@ def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, pr
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  llm_name = list_llm[llm_option]
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  print("llm_name: ",llm_name)
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  qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
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- return qa_chain, "QA chain Taiyar hai. Ab swaal pooch sakti ho!"
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  def format_chat_history(message, chat_history):
@@ -138,16 +138,16 @@ def demo():
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  vector_db = gr.State()
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  qa_chain = gr.State()
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  gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
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- gr.Markdown("""<b>Apne PDF k related swaal poochiye!</b> This AI agent is designed to assist Pooja only to help her with the PDF documents. The app is hosted here for the sole purpose of demonstration. \
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  <b>Please do not upload confidential documents.</b>
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  """)
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  with gr.Row():
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  with gr.Column(scale = 86):
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- gr.Markdown("<b>Step 1 - Yaha apni PDF Upload karo..</b>")
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  with gr.Row():
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  document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
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  with gr.Row():
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- db_btn = gr.Button("Padho Ye PDFs!!!")
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  with gr.Row():
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  db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status",
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  gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
@@ -167,7 +167,7 @@ def demo():
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  llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status",
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  with gr.Column(scale = 200):
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- gr.Markdown("<b>Step 2 - Dinesh se na sahi, PDF se baatein karo</b>")
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  chatbot = gr.Chatbot(height=505)
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  with gr.Accordion("Relevent context from the source document", open=False):
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  with gr.Row():
@@ -182,7 +182,7 @@ def demo():
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  with gr.Row():
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  msg = gr.Textbox(placeholder="Ask a question", container=True)
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  with gr.Row():
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- submit_btn = gr.Button("Ab Batao..!")
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  clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
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  # Preprocessing events
 
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  llm_name = list_llm[llm_option]
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  print("llm_name: ",llm_name)
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  qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
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+ return qa_chain, "QA chain initialized. Chatbot is ready!"
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  def format_chat_history(message, chat_history):
 
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  vector_db = gr.State()
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  qa_chain = gr.State()
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  gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
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+ gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
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  <b>Please do not upload confidential documents.</b>
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  """)
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  with gr.Row():
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  with gr.Column(scale = 86):
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+ gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
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  with gr.Row():
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  document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
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  with gr.Row():
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+ db_btn = gr.Button("Create vector database")
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  with gr.Row():
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  db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status",
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  gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
 
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  llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status",
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  with gr.Column(scale = 200):
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+ gr.Markdown("<b>Step 2 - Chat with your Document</b>")
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  chatbot = gr.Chatbot(height=505)
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  with gr.Accordion("Relevent context from the source document", open=False):
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  with gr.Row():
 
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  with gr.Row():
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  msg = gr.Textbox(placeholder="Ask a question", container=True)
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  with gr.Row():
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+ submit_btn = gr.Button("Submit")
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  clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
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  # Preprocessing events