Upload 10 files

Dockerfile.txt

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+FROM python:3.10
+
+WORKDIR /src
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH \
+    PYTHONPATH=$HOME/app \
+	PYTHONUNBUFFERED=1 \
+	GRADIO_ALLOW_FLAGGING=never \
+	GRADIO_NUM_PORTS=1 \
+	GRADIO_SERVER_NAME=0.0.0.0 \
+	GRADIO_THEME=huggingface \
+	SYSTEM=spaces
+ 
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["python", "app.py"]

LICENSE.txt

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Link to images.txt

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+Robot emoji: https://commons.wikimedia.org/wiki/File:Fluent_Emoji_high_contrast_1f916.svg
+
+Bing smile emoji: https://www.bing.com/images/create/a-black-and-white-emoji-with-a-simple-smile2c-black/6523d2c320df409581e85bec80ef3ba8?id=KTdVbixG8oRqR9BzF6AblQ%3d%3d&view=detailv2&idpp=genimg&idpclose=1&FORM=SYDBIC
+

README.md

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+---
+title: Light PDF web QA chatbot
+emoji: 🌍
+colorFrom: yellow
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Chat with a pdf file or web page using a light language model through a Gradio interface. Quick responses even just using CPU.

app.py

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+# # Load in packages
+
+# +
+import os
+
+# Need to overwrite version of gradio present in Huggingface spaces as it doesn't have like buttons/avatars (Oct 2023)
+#os.system("pip uninstall -y gradio")
+os.system("pip install gradio==3.42.0")
+
+from typing import TypeVar
+from langchain.embeddings import HuggingFaceEmbeddings#, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+import gradio as gr
+
+from transformers import AutoTokenizer
+
+# Alternative model sources
+from ctransformers import AutoModelForCausalLM
+
+PandasDataFrame = TypeVar('pd.core.frame.DataFrame')
+
+# Disable cuda devices if necessary
+#os.environ['CUDA_VISIBLE_DEVICES'] = '-1' 
+
+#from chatfuncs.chatfuncs import *
+import chatfuncs.ingest as ing
+
+##  Load preset embeddings, vectorstore, and model
+
+embeddings_name = "BAAI/bge-base-en-v1.5"
+
+def load_embeddings(embeddings_name = "BAAI/bge-base-en-v1.5"):
+
+
+    #if embeddings_name == "hkunlp/instructor-large":
+    #    embeddings_func = HuggingFaceInstructEmbeddings(model_name=embeddings_name,
+    #    embed_instruction="Represent the paragraph for retrieval: ",
+    #    query_instruction="Represent the question for retrieving supporting documents: "
+    #    )
+
+    #else: 
+    embeddings_func = HuggingFaceEmbeddings(model_name=embeddings_name)
+
+    global embeddings
+
+    embeddings = embeddings_func
+
+    return embeddings
+
+def get_faiss_store(faiss_vstore_folder,embeddings):
+    import zipfile
+    with zipfile.ZipFile(faiss_vstore_folder + '/' + faiss_vstore_folder + '.zip', 'r') as zip_ref:
+        zip_ref.extractall(faiss_vstore_folder)
+
+    faiss_vstore = FAISS.load_local(folder_path=faiss_vstore_folder, embeddings=embeddings)
+    os.remove(faiss_vstore_folder + "/index.faiss")
+    os.remove(faiss_vstore_folder + "/index.pkl")
+    
+    global vectorstore
+
+    vectorstore = faiss_vstore
+
+    return vectorstore
+
+import chatfuncs.chatfuncs as chatf
+
+chatf.embeddings = load_embeddings(embeddings_name)
+chatf.vectorstore = get_faiss_store(faiss_vstore_folder="faiss_embedding",embeddings=globals()["embeddings"])
+
+def load_model(model_type, gpu_layers, gpu_config=None, cpu_config=None, torch_device=None):
+    print("Loading model")
+
+    # Default values inside the function
+    if gpu_config is None:
+        gpu_config = chatf.gpu_config
+    if cpu_config is None:
+        cpu_config = chatf.cpu_config
+    if torch_device is None:
+        torch_device = chatf.torch_device
+
+    if model_type == "Mistral Open Orca (larger, slow)":
+        if torch_device == "cuda":
+            gpu_config.update_gpu(gpu_layers)
+        else:
+            gpu_config.update_gpu(gpu_layers)
+            cpu_config.update_gpu(gpu_layers)
+
+        print("Loading with", cpu_config.gpu_layers, "model layers sent to GPU.")
+
+        print(vars(gpu_config))
+        print(vars(cpu_config))
+
+        try:
+            #model = AutoModelForCausalLM.from_pretrained('Aryanne/Orca-Mini-3B-gguf', model_type='llama', model_file='q5_0-orca-mini-3b.gguf', **vars(gpu_config)) # **asdict(CtransRunConfig_cpu())
+            #model = AutoModelForCausalLM.from_pretrained('Aryanne/Wizard-Orca-3B-gguf', model_type='llama', model_file='q4_1-wizard-orca-3b.gguf', **vars(gpu_config)) # **asdict(CtransRunConfig_cpu())
+            model = AutoModelForCausalLM.from_pretrained('TheBloke/Mistral-7B-OpenOrca-GGUF', model_type='mistral', model_file='mistral-7b-openorca.Q4_K_M.gguf', **vars(gpu_config)) # **asdict(CtransRunConfig_cpu())
+            #model = AutoModelForCausalLM.from_pretrained('TheBloke/MistralLite-7B-GGUF', model_type='mistral', model_file='mistrallite.Q4_K_M.gguf', **vars(gpu_config)) # **asdict(CtransRunConfig_cpu())
+        
+        except:
+            #model = AutoModelForCausalLM.from_pretrained('Aryanne/Orca-Mini-3B-gguf', model_type='llama', model_file='q5_0-orca-mini-3b.gguf', **vars(cpu_config)) #**asdict(CtransRunConfig_gpu())
+            #model = AutoModelForCausalLM.from_pretrained('Aryanne/Wizard-Orca-3B-gguf', model_type='llama', model_file='q4_1-wizard-orca-3b.gguf', **vars(cpu_config)) # **asdict(CtransRunConfig_cpu())
+            model = AutoModelForCausalLM.from_pretrained('TheBloke/Mistral-7B-OpenOrca-GGUF', model_type='mistral', model_file='mistral-7b-openorca.Q4_K_M.gguf', **vars(cpu_config)) # **asdict(CtransRunConfig_cpu())
+            #model = AutoModelForCausalLM.from_pretrained('TheBloke/MistralLite-7B-GGUF', model_type='mistral', model_file='mistrallite.Q4_K_M.gguf', **vars(cpu_config)) # **asdict(CtransRunConfig_cpu())
+
+        tokenizer = []
+
+    if model_type == "Flan Alpaca (small, fast)":
+        # Huggingface chat model
+        hf_checkpoint = 'declare-lab/flan-alpaca-large'#'declare-lab/flan-alpaca-base' # # #
+        
+        def create_hf_model(model_name):
+
+            from transformers import AutoModelForSeq2SeqLM,  AutoModelForCausalLM
+            
+            if torch_device == "cuda":
+                if "flan" in model_name:
+                    model = AutoModelForSeq2SeqLM.from_pretrained(model_name, device_map="auto")
+                else:
+                    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+            else:
+                if "flan" in model_name:
+                    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+                else: 
+                    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+            tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length = chatf.context_length)
+
+            return model, tokenizer, model_type
+
+        model, tokenizer, model_type = create_hf_model(model_name = hf_checkpoint)
+
+    chatf.model = model
+    chatf.tokenizer = tokenizer
+    chatf.model_type = model_type
+
+    load_confirmation = "Finished loading model: " + model_type
+
+    print(load_confirmation)
+    return model_type, load_confirmation, model_type
+
+# Both models are loaded on app initialisation so that users don't have to wait for the models to be downloaded
+#model_type = "Mistral Open Orca (larger, slow)"
+#load_model(model_type, chatf.gpu_layers, chatf.gpu_config, chatf.cpu_config, chatf.torch_device)
+
+model_type = "Flan Alpaca (small, fast)"
+load_model(model_type, 0, chatf.gpu_config, chatf.cpu_config, chatf.torch_device)
+
+def docs_to_faiss_save(docs_out:PandasDataFrame, embeddings=embeddings):
+
+    print(f"> Total split documents: {len(docs_out)}")
+
+    print(docs_out)
+
+    vectorstore_func = FAISS.from_documents(documents=docs_out, embedding=embeddings)
+
+
+    chatf.vectorstore = vectorstore_func
+
+    out_message = "Document processing complete"
+
+    return out_message, vectorstore_func
+
+ # Gradio chat
+
+block = gr.Blocks(theme = gr.themes.Base())#css=".gradio-container {background-color: black}")
+
+with block:
+    ingest_text = gr.State()
+    ingest_metadata = gr.State()
+    ingest_docs = gr.State()
+
+    model_type_state = gr.State(model_type)
+    embeddings_state = gr.State(globals()["embeddings"])
+    vectorstore_state = gr.State(globals()["vectorstore"])  
+
+    model_state = gr.State() # chatf.model (gives error)
+    tokenizer_state = gr.State() # chatf.tokenizer (gives error)
+
+    chat_history_state = gr.State()
+    instruction_prompt_out = gr.State()
+
+    gr.Markdown("<h1><center>Lightweight PDF / web page QA bot</center></h1>")        
+    
+    gr.Markdown("Chat with PDF, web page or (new) csv/Excel documents. The default is a small model (Flan Alpaca), that can only answer specific questions that are answered in the text. It cannot give overall impressions of, or summarise the document. The alternative (Mistral Open Orca (larger, slow)), can reason a little better, but is much slower (See Advanced tab).\n\nBy default the Lambeth Borough Plan '[Lambeth 2030 : Our Future, Our Lambeth](https://www.lambeth.gov.uk/better-fairer-lambeth/projects/lambeth-2030-our-future-our-lambeth)' is loaded. If you want to talk about another document or web page, please select from the second tab. If switching topic, please click the 'Clear chat' button.\n\nCaution: This is a public app. Please ensure that the document you upload is not sensitive is any way as other users may see it! Also, please note that LLM chatbots may give incomplete or incorrect information, so please use with care.")
+
+    with gr.Row():
+        current_source = gr.Textbox(label="Current data source(s)", value="Lambeth_2030-Our_Future_Our_Lambeth.pdf", scale = 10)
+        current_model = gr.Textbox(label="Current model", value=model_type, scale = 3)
+
+    with gr.Tab("Chatbot"):
+
+        with gr.Row():
+            #chat_height = 500
+            chatbot = gr.Chatbot(avatar_images=('user.jfif', 'bot.jpg'),bubble_full_width = False, scale = 1) # , height=chat_height
+            with gr.Accordion("Open this tab to see the source paragraphs used to generate the answer", open = False):
+                sources = gr.HTML(value = "Source paragraphs with the most relevant text will appear here", scale = 1) # , height=chat_height
+
+        with gr.Row():
+            message = gr.Textbox(
+                label="Enter your question here",
+                lines=1,
+            )     
+        with gr.Row():
+            submit = gr.Button(value="Send message", variant="secondary", scale = 1)
+            clear = gr.Button(value="Clear chat", variant="secondary", scale=0) 
+            stop = gr.Button(value="Stop generating", variant="secondary", scale=0)
+
+        examples_set = gr.Radio(label="Examples for the Lambeth Borough Plan",
+            #value = "What were the five pillars of the previous borough plan?",
+            choices=["What were the five pillars of the previous borough plan?",
+                "What is the vision statement for Lambeth?",
+                "What are the commitments for Lambeth?",
+                "What are the 2030 outcomes for Lambeth?"])
+
+        
+        current_topic = gr.Textbox(label="Feature currently disabled - Keywords related to current conversation topic.", placeholder="Keywords related to the conversation topic will appear here")
+            
+
+
+    with gr.Tab("Load in a different file to chat with"):
+        with gr.Accordion("PDF file", open = False):
+            in_pdf = gr.File(label="Upload pdf", file_count="multiple", file_types=['.pdf'])
+            load_pdf = gr.Button(value="Load in file", variant="secondary", scale=0)
+        
+        with gr.Accordion("Web page", open = False):
+            with gr.Row():
+                in_web = gr.Textbox(label="Enter web page url")
+                in_div = gr.Textbox(label="(Advanced) Web page div for text extraction", value="p", placeholder="p")
+            load_web = gr.Button(value="Load in webpage", variant="secondary", scale=0)
+
+        with gr.Accordion("CSV/Excel file", open = False):
+            in_csv = gr.File(label="Upload CSV/Excel file", file_count="multiple", file_types=['.csv', '.xlsx'])
+            in_text_column = gr.Textbox(label="Enter column name where text is stored")
+            load_csv = gr.Button(value="Load in CSV/Excel file", variant="secondary", scale=0)
+        
+        ingest_embed_out = gr.Textbox(label="File/web page preparation progress")
+
+    with gr.Tab("Advanced features"):
+        out_passages = gr.Slider(minimum=1, value = 2, maximum=10, step=1, label="Choose number of passages to retrieve from the document. Numbers greater than 2 may lead to increased hallucinations or input text being truncated.")
+        temp_slide = gr.Slider(minimum=0.1, value = 0.1, maximum=1, step=0.1, label="Choose temperature setting for response generation.")
+        with gr.Row():
+            model_choice = gr.Radio(label="Choose a chat model", value="Flan Alpaca (small, fast)", choices = ["Flan Alpaca (small, fast)", "Mistral Open Orca (larger, slow)"])
+            change_model_button = gr.Button(value="Load model", scale=0)
+        with gr.Accordion("Choose number of model layers to send to GPU (WARNING: please don't modify unless you are sure you have a GPU).", open = False):
+            gpu_layer_choice = gr.Slider(label="Choose number of model layers to send to GPU.", value=0, minimum=0, maximum=5, step = 1, visible=True)
+            
+        load_text = gr.Text(label="Load status")
+        
+
+    gr.HTML(
+        "<center>This app is based on the models Flan Alpaca and Mistral Open Orca. It powered by Gradio, Transformers, Ctransformers, and Langchain.</a></center>"
+    )
+
+    examples_set.change(fn=chatf.update_message, inputs=[examples_set], outputs=[message])
+
+    change_model_button.click(fn=chatf.turn_off_interactivity, inputs=[message, chatbot], outputs=[message, chatbot], queue=False).\
+    then(fn=load_model, inputs=[model_choice, gpu_layer_choice], outputs = [model_type_state, load_text, current_model]).\
+    then(lambda: chatf.restore_interactivity(), None, [message], queue=False).\
+    then(chatf.clear_chat, inputs=[chat_history_state, sources, message, current_topic], outputs=[chat_history_state, sources, message, current_topic]).\
+    then(lambda: None, None, chatbot, queue=False)
+
+    # Load in a pdf
+    load_pdf_click = load_pdf.click(ing.parse_file, inputs=[in_pdf], outputs=[ingest_text, current_source]).\
+             then(ing.text_to_docs, inputs=[ingest_text], outputs=[ingest_docs]).\
+             then(docs_to_faiss_save, inputs=[ingest_docs], outputs=[ingest_embed_out, vectorstore_state]).\
+             then(chatf.hide_block, outputs = [examples_set])
+
+    # Load in a webpage
+    load_web_click = load_web.click(ing.parse_html, inputs=[in_web, in_div], outputs=[ingest_text, ingest_metadata, current_source]).\
+             then(ing.html_text_to_docs, inputs=[ingest_text, ingest_metadata], outputs=[ingest_docs]).\
+             then(docs_to_faiss_save, inputs=[ingest_docs], outputs=[ingest_embed_out, vectorstore_state]).\
+             then(chatf.hide_block, outputs = [examples_set])
+    
+    # Load in a csv/excel file
+    load_csv_click = load_csv.click(ing.parse_csv_or_excel, inputs=[in_csv, in_text_column], outputs=[ingest_text, current_source]).\
+             then(ing.csv_excel_text_to_docs, inputs=[ingest_text, in_text_column], outputs=[ingest_docs]).\
+             then(docs_to_faiss_save, inputs=[ingest_docs], outputs=[ingest_embed_out, vectorstore_state]).\
+             then(chatf.hide_block, outputs = [examples_set])
+
+    # Load in a webpage
+
+    # Click/enter to send message action
+    response_click = submit.click(chatf.create_full_prompt, inputs=[message, chat_history_state, current_topic, vectorstore_state, embeddings_state, model_type_state, out_passages], outputs=[chat_history_state, sources, instruction_prompt_out], queue=False, api_name="retrieval").\
+                then(chatf.turn_off_interactivity, inputs=[message, chatbot], outputs=[message, chatbot], queue=False).\
+                then(chatf.produce_streaming_answer_chatbot, inputs=[chatbot, instruction_prompt_out, model_type_state, temp_slide], outputs=chatbot)
+    response_click.then(chatf.highlight_found_text, [chatbot, sources], [sources]).\
+                then(chatf.add_inputs_answer_to_history,[message, chatbot, current_topic], [chat_history_state, current_topic]).\
+                then(lambda: chatf.restore_interactivity(), None, [message], queue=False)
+
+    response_enter = message.submit(chatf.create_full_prompt, inputs=[message, chat_history_state, current_topic, vectorstore_state, embeddings_state, model_type_state, out_passages], outputs=[chat_history_state, sources, instruction_prompt_out], queue=False).\
+                then(chatf.turn_off_interactivity, inputs=[message, chatbot], outputs=[message, chatbot], queue=False).\
+                then(chatf.produce_streaming_answer_chatbot, [chatbot, instruction_prompt_out, model_type_state, temp_slide], chatbot)    
+    response_enter.then(chatf.highlight_found_text, [chatbot, sources], [sources]).\
+                then(chatf.add_inputs_answer_to_history,[message, chatbot, current_topic], [chat_history_state, current_topic]).\
+                then(lambda: chatf.restore_interactivity(), None, [message], queue=False)
+    
+    # Stop box
+    stop.click(fn=None, inputs=None, outputs=None, cancels=[response_click, response_enter])
+
+    # Clear box
+    clear.click(chatf.clear_chat, inputs=[chat_history_state, sources, message, current_topic], outputs=[chat_history_state, sources, message, current_topic])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+    # Thumbs up or thumbs down voting function
+    chatbot.like(chatf.vote, [chat_history_state, instruction_prompt_out, model_type_state], None)
+
+block.queue(concurrency_count=1).launch(debug=True)
+# -
+

gitignore.txt

@@ -0,0 +1,10 @@
+*.pyc
+*.ipynb
+*.pdf
+*.spec
+*.toc
+*.csv
+bootstrapper.py
+build/*
+dist/*
+Q tests/*

requirements.txt

@@ -0,0 +1,16 @@
+langchain
+beautifulsoup4
+pandas
+transformers
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+sentence_transformers
+faiss-cpu
+pypdf
+python-docx
+ctransformers[cuda]
+keybert
+span_marker
+gensim
+gradio==3.42.0
+gradio_client