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seawolf2357 commited on May 16

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-import torch
-import transformers
-import gradio as gr
-from ragatouille import RAGPretrainedModel
-from huggingface_hub import InferenceClient
-import re
-from datetime import datetime
-import json
-import arxiv
-from utils import get_md_text_abstract, search_cleaner, get_arxiv_live_search
-retrieve_results = 20
-show_examples = True
-llm_models_to_choose = ['mistralai/Mixtral-8x7B-Instruct-v0.1','mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None']
-generate_kwargs = dict(
-    temperature = None,
-    max_new_tokens = 512,
-    top_p = None,
-    do_sample = False,
-    )
-## RAG Model
-RAG = RAGPretrainedModel.from_index("colbert/indexes/arxiv_colbert")
-try:
-  gr.Info("Setting up retriever, please wait...")
-  rag_initial_output = RAG.search("What is Generative AI in Healthcare?", k = 1)
-  gr.Info("Retriever working successfully!")
-except:
-  gr.Warning("Retriever not working!")
-## Header
-mark_text = '# 🩺🔍 Search Results\n'
-header_text = "## Arxiv Paper Summary With QA Retrieval Augmented Generation \n"
-try:
-  with open("README.md", "r") as f:
-      mdfile = f.read()
-  date_pattern = r'Index Last Updated : \d{4}-\d{2}-\d{2}'
-  match = re.search(date_pattern, mdfile)
-  date = match.group().split(': ')[1]
-  formatted_date = datetime.strptime(date, '%Y-%m-%d').strftime('%d %b %Y')
-  header_text += f'Index Last Updated: {formatted_date}\n'
-  index_info = f"Semantic Search - up to {formatted_date}"
-except:
-  index_info = "Semantic Search"
-database_choices = [index_info,'Arxiv Search - Latest - (EXPERIMENTAL)']
-## Arxiv API
-arx_client = arxiv.Client()
-is_arxiv_available = True
-check_arxiv_result = get_arxiv_live_search("What is Self Rewarding AI and how can it be used in Multi-Agent Systems?", arx_client, retrieve_results)
-if len(check_arxiv_result) == 0:
-  is_arxiv_available = False
-  print("Arxiv search not working, switching to default search ...")
-  database_choices = [index_info]
-## Show examples
-sample_outputs = {
-    'output_placeholder': 'The LLM will provide an answer to your question here...',
-    'search_placeholder': '''
-1. What is MoE?
-2. What are Multi Agent Systems?
-3. What is Self Rewarding AI?
-4. What is Semantic and Episodic memory?
-5. What is AutoGen?
-6. What is ChatDev?
-7. What is Omniverse?
-8. What is Lumiere?
-9. What is SORA?
-'''
-}
-output_placeholder = sample_outputs['output_placeholder']
-md_text_initial = sample_outputs['search_placeholder']
-def rag_cleaner(inp):
-    rank = inp['rank']
-    title = inp['document_metadata']['title']
-    content = inp['content']
-    date = inp['document_metadata']['_time']
-    return f"{rank}. <b> {title} </b> \n Date : {date} \n Abstract: {content}"
-def get_prompt_text(question, context, formatted = True, llm_model_picked = 'mistralai/Mistral-7B-Instruct-v0.2'):
-    if formatted:
-      sys_instruction = f"Context:\n {context} \n Given the following scientific paper abstracts, take a deep breath and lets think step by step to answer the question. Cite the titles of your sources when answering, do not cite links or dates."
-      message = f"Question: {question}"
-      if 'mistralai' in llm_model_picked:
-          return f"<s>" + f"[INST] {sys_instruction}" +  f" {message}[/INST]"
-      elif 'gemma' in llm_model_picked:
-          return f"<bos><start_of_turn>user\n{sys_instruction}" +  f" {message}<end_of_turn>\n"
-    return f"Context:\n {context} \n Given the following info, take a deep breath and lets think step by step to answer the question: {question}. Cite the titles of your sources when answering.\n\n"
-def get_references(question, retriever, k = retrieve_results):
-    rag_out = retriever.search(query=question, k=k)
-    return rag_out
-def get_rag(message):
-    return get_references(message, RAG)
-def SaveResponseAndRead(result):
-    documentHTML5='''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>Read It Aloud</title>
-        <script type="text/javascript">
-            function readAloud() {
-                const text = document.getElementById("textArea").value;
-                const speech = new SpeechSynthesisUtterance(text);
-                window.speechSynthesis.speak(speech);
-            }
-        </script>
-    </head>
-    <body>
-        <h1>🔊 Read It Aloud</h1>
-        <textarea id="textArea" rows="10" cols="80">
-    '''
-    documentHTML5 = documentHTML5 + result
-    documentHTML5 = documentHTML5 + '''
-        </textarea>
-        <br>
-        <button onclick="readAloud()">🔊 Read Aloud</button>
-    </body>
-    </html>
-    '''
-    gr.HTML(documentHTML5)
-with gr.Blocks(theme = gr.themes.Soft()) as demo:
-    header = gr.Markdown(header_text)
-    with gr.Group():
-      msg = gr.Textbox(label = 'Search', placeholder = 'What is Mistral?')
-      with gr.Accordion("Advanced Settings", open=False):
-        with gr.Row(equal_height = True):
-          llm_model = gr.Dropdown(choices = llm_models_to_choose, value = 'mistralai/Mistral-7B-Instruct-v0.2', label = 'LLM Model')
-          llm_results = gr.Slider(minimum=4, maximum=10, value=5, step=1, interactive=True, label="Top n results as context")
-          database_src = gr.Dropdown(choices = database_choices, value = index_info, label = 'Search Source')
-          stream_results = gr.Checkbox(value = True, label = "Stream output", visible = False)
-    output_text = gr.Textbox(show_label = True, container = True, label = 'LLM Answer', visible = True, placeholder = output_placeholder)
-    input = gr.Textbox(show_label = False, visible = False)
-    gr_md = gr.Markdown(mark_text + md_text_initial)
-    def update_with_rag_md(message, llm_results_use = 5, database_choice = index_info, llm_model_picked = 'mistralai/Mistral-7B-Instruct-v0.2'):
-        prompt_text_from_data = ""
-        database_to_use = database_choice
-        if database_choice == index_info:
-          rag_out = get_rag(message)
-        else:
-          arxiv_search_success = True
-          try:
-            rag_out = get_arxiv_live_search(message, arx_client, retrieve_results)
-            if len(rag_out) == 0:
-              arxiv_search_success = False
-          except:
-            arxiv_search_success = False
-          if not arxiv_search_success:
-            gr.Warning("Arxiv Search not working, switching to semantic search ...")
-            rag_out = get_rag(message)
-            database_to_use = index_info
-        md_text_updated = mark_text
-        for i in range(retrieve_results):
-          rag_answer = rag_out[i]
-          if i < llm_results_use:
-            md_text_paper, prompt_text = get_md_text_abstract(rag_answer, source = database_to_use, return_prompt_formatting = True)
-            prompt_text_from_data += f"{i+1}. {prompt_text}"
-          else:
-            md_text_paper = get_md_text_abstract(rag_answer, source = database_to_use)
-          md_text_updated += md_text_paper
-        prompt = get_prompt_text(message, prompt_text_from_data, llm_model_picked = llm_model_picked)
-        return md_text_updated, prompt
-    def ask_llm(prompt, llm_model_picked = 'mistralai/Mistral-7B-Instruct-v0.2', stream_outputs = False):
-       model_disabled_text = "LLM Model is disabled"
-       output = ""
-       if llm_model_picked == 'None':
-          if stream_outputs:
-              for out in model_disabled_text:
-                output += out
-                yield output
-              return output
-          else:
-              return model_disabled_text
-       client = InferenceClient(llm_model_picked)
-       try:
-           stream = client.text_generation(prompt, **generate_kwargs,  stream=stream_outputs, details=False, return_full_text=False)
-       except:
-           gr.Warning("LLM Inference rate limit reached, try again later!")
-           return ""
-       if stream_outputs:
-           for response in stream:
-              output += response
-              SaveResponseAndRead(response)
-              yield output
-           return output
-       else:
-           return stream
-    msg.submit(update_with_rag_md, [msg, llm_results,  database_src, llm_model], [gr_md, input]).success(ask_llm, [input, llm_model, stream_results], output_text)
-demo.queue().launch()