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import os
import gradio as gr
import argparse
from functools import partial
from string import Template
from utils import load_prompt, setup_gemini_client
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--ai-studio-api-key", type=str, default=os.getenv("GEMINI_API_KEY"))
parser.add_argument("--vertexai", action="store_true", default=False)
parser.add_argument("--vertexai-project", type=str, default="gcp-ml-172005")
parser.add_argument("--vertexai-location", type=str, default="us-central1")
parser.add_argument("--model", type=str, default="gemini-1.5-flash")
parser.add_argument("--prompt-tmpl-path", type=str, default="configs/prompts.toml")
parser.add_argument("--css-path", type=str, default="statics/styles.css")
args = parser.parse_args()
return args
def find_attached_file(filename, attached_files):
for file in attached_files:
if file['name'] == filename:
return file
return None
def echo(message, history, state):
summary = ""
attached_file = None
if message['files']:
path_local = message['files'][0]
filename = os.path.basename(path_local)
attached_file = find_attached_file(filename, state["attached_files"])
if attached_file is None:
path_gcp = client.files.upload(path=path_local)
state["attached_files"].append({
"name": filename,
"path_local": path_local,
"gcp_entity": path_gcp,
"path_gcp": path_gcp.name,
"mime_type=": path_gcp.mime_type,
"expiration_time": path_gcp.expiration_time,
})
attached_file = path_gcp
# [{'role': 'user', 'metadata': None, 'content': 'asdf', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': 'asdf', 'options': None}]
user_message = [message['text']]
if attached_file: user_message.append(attached_file)
chat_history = state['messages']
chat_history = chat_history + user_message
state['messages'] = chat_history
response = client.models.generate_content(
model="gemini-1.5-flash",
contents=state['messages']
)
# make summary
if state['summary'] == "":
state['summary'] = response.text
else:
response = client.models.generate_content(
model="gemini-1.5-flash",
contents=[
Template(
prompt_tmpl['summarization']['prompt']
).safe_substitute(
previous_summary=state['summary'],
latest_conversation=str({"user": message['text'], "assistant": response.text})
)
]
)
state['summary'] = response.text
return response.text, state, state['summary']
def main(args):
style_css = open(args.css_path, "r").read()
global client, prompt_tmpl
client = setup_gemini_client(args)
prompt_tmpl = load_prompt(args)
## Gradio Blocks
with gr.Blocks(css=style_css) as demo:
# State per session
state = gr.State({
"messages": [],
"attached_files": [],
"summary": ""
})
gr.Markdown("# Adaptive Summarization")
gr.Markdown("AdaptSum stands for Adaptive Summarization. This project focuses on developing an LLM-powered system for dynamic summarization. Instead of generating entirely new summaries with each update, the system intelligently identifies and modifies only the necessary parts of the existing summary. This approach aims to create a more efficient and fluid summarization process within a continuous chat interaction with an LLM.")
with gr.Row(elem_id="chat-interface"):
with gr.Column(scale=3, elem_id="summary-window"):
summary = gr.Markdown(label="Summary so far")
with gr.Column(scale=7):
gr.ChatInterface(
multimodal=True,
type="messages",
fn=echo,
additional_inputs=[state],
additional_outputs=[state, summary],
)
return demo
if __name__ == "__main__":
args = parse_args()
demo = main(args)
demo.launch()
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