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import threading
import http.server
import socketserver
import os
import yaml
from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import gradio as gr
from utils.upload_file import UploadFile
from utils.chatbot import ChatBot
from utils.ui_settings import UISettings
from utils.load_config import LoadConfig
from pyprojroot import here
# Load the app config
with open(here("configs/app_config.yml")) as cfg:
app_config = yaml.load(cfg, Loader=yaml.FullLoader)
PORT = app_config["serve"]["port"]
DIRECTORY1 = app_config["directories"]["data_directory"]
DIRECTORY2 = app_config["directories"]["data_directory_2"]
# ================================
# Part 1: Reference Serve Code
# ================================
class MultiDirectoryHTTPRequestHandler(http.server.SimpleHTTPRequestHandler):
"""Serve files from multiple directories."""
def translate_path(self, path):
parts = path.split('/', 2)
if len(parts) > 1:
first_directory = parts[1]
if first_directory == os.path.basename(DIRECTORY1):
path = os.path.join(DIRECTORY1, *parts[2:])
elif first_directory == os.path.basename(DIRECTORY2):
path = os.path.join(DIRECTORY2, *parts[2:])
else:
file_path1 = os.path.join(DIRECTORY1, first_directory)
file_path2 = os.path.join(DIRECTORY2, first_directory)
if os.path.isfile(file_path1):
return file_path1
elif os.path.isfile(file_path2):
return file_path2
return super().translate_path(path)
def start_reference_server():
with socketserver.TCPServer(("", PORT), MultiDirectoryHTTPRequestHandler) as httpd:
print(f"Serving at port {PORT}")
httpd.serve_forever()
# ================================
# Part 2: LLM Serve Code
# ================================
APPCFG = LoadConfig()
app = Flask(__name__)
# Load the LLM and tokenizer
tokenizer = AutoTokenizer.from_pretrained(
APPCFG.llm_engine, token=APPCFG.gemma_token, device=APPCFG.device)
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="BioMistral/BioMistral-7B",
token=APPCFG.gemma_token,
torch_dtype=torch.float16,
device_map=APPCFG.device)
app_pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
@app.route("/generate_text", methods=["POST"])
def generate_text():
data = request.json
prompt = data.get("prompt", "")
max_new_tokens = data.get("max_new_tokens", 1000)
do_sample = data.get("do_sample", True)
temperature = data.get("temperature", 0.1)
top_k = data.get("top_k", 50)
top_p = data.get("top_p", 0.95)
tokenized_prompt = app_pipeline.tokenizer.apply_chat_template(
prompt, tokenize=False, add_generation_prompt=True)
outputs = app_pipeline(
tokenized_prompt,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
top_p=top_p
)
return jsonify({"response": outputs[0]["generated_text"][len(tokenized_prompt):]})
def start_llm_server():
app.run(debug=False, port=8888)
# ================================
# Part 3: Gradio Chatbot Code
# ================================
def start_gradio_app():
with gr.Blocks() as demo:
with gr.Tabs():
with gr.TabItem("Med-App"):
# First row
with gr.Row() as row_one:
with gr.Column(visible=False) as reference_bar:
ref_output = gr.Markdown()
with gr.Column() as chatbot_output:
chatbot = gr.Chatbot(
[], elem_id="chatbot", bubble_full_width=False, height=500,
avatar_images=("images/test.png", "images/Gemma-logo.png")
)
chatbot.like(UISettings.feedback, None, None)
# Second row
with gr.Row():
input_txt = gr.Textbox(
lines=4, scale=8, placeholder="Enter text and press enter, or upload PDF files"
)
# Third row
with gr.Row() as row_two:
text_submit_btn = gr.Button(value="Submit text")
btn_toggle_sidebar = gr.Button(value="References")
upload_btn = gr.UploadButton(
"📁 Upload PDF or doc files", file_types=['.pdf', '.doc'], file_count="multiple"
)
clear_button = gr.ClearButton([input_txt, chatbot])
rag_with_dropdown = gr.Dropdown(
label="RAG with", choices=["Preprocessed doc", "Upload doc: Process for RAG"], value="Preprocessed doc"
)
# Fourth row
with gr.Row() as row_four:
temperature_bar = gr.Slider(
minimum=0.1, maximum=1, value=0.1, step=0.1, label="Temperature",
info="Increasing the temperature will make the model answer more creatively."
)
top_k = gr.Slider(
minimum=0.0, maximum=100.0, step=1, label="top_k", value=50,
info="A lower value (e.g. 10) will result in more conservative answers."
)
top_p = gr.Slider(
minimum=0.0, maximum=1.0, step=0.01, label="top_p", value=0.95,
info="A lower value will generate more focused and conservative text."
)
# Process uploaded files and text
file_msg = upload_btn.upload(
fn=UploadFile.process_uploaded_files, inputs=[upload_btn, chatbot, rag_with_dropdown],
outputs=[input_txt, chatbot], queue=False
)
txt_msg = input_txt.submit(
fn=ChatBot.respond, inputs=[chatbot, input_txt, rag_with_dropdown, temperature_bar, top_k, top_p],
outputs=[input_txt, chatbot, ref_output], queue=False
).then(lambda: gr.Textbox(interactive=True), None, [input_txt], queue=False)
text_submit_btn.click(
fn=ChatBot.respond, inputs=[chatbot, input_txt, rag_with_dropdown, temperature_bar, top_k, top_p],
outputs=[input_txt, chatbot, ref_output], queue=False
).then(lambda: gr.Textbox(interactive=True), None, [input_txt], queue=False)
demo.launch()
# ================================
# Main: Running all services concurrently
# ================================
if __name__ == "__main__":
# Start all services in separate threads
reference_server_thread = threading.Thread(target=start_reference_server)
llm_server_thread = threading.Thread(target=start_llm_server)
gradio_app_thread = threading.Thread(target=start_gradio_app)
reference_server_thread.start()
llm_server_thread.start()
gradio_app_thread.start()
# Keep the main thread alive
reference_server_thread.join()
llm_server_thread.join()
gradio_app_thread.join()
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