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import gradio as gr
import os
import importlib
import sys
from algos.PWS import *
from utils.util import *
from nodes.Worker import *
from prompts import fewshots
# Load API keys
with open(os.path.join('./keys/', 'openai.key'), 'r') as f:
os.environ["OPENAI_API_KEY"] = f.read().strip()
with open(os.path.join('./keys/', 'serpapi.key'), 'r') as f:
os.environ["SERPAPI_API_KEY"] = f.read().strip()
def reload_modules():
"""Reload all relevant modules"""
importlib.reload(sys.modules['nodes.Worker'])
importlib.reload(sys.modules['algos.PWS'])
importlib.reload(sys.modules['utils.util'])
importlib.reload(sys.modules['prompts.fewshots'])
importlib.reload(sys.modules['prompts.solver'])
return "✅ Modules reloaded successfully!"
def process(tools, model, input_text):
# Use study abroad fewshot for study-related questions
if any(word in input_text.lower() for word in ["study", "student", "university", "college", "school", "abroad", "học", "trường", "du học", "học bổng", "gpa", "ielts", "tcf", "delf", "scholarship"]):
# Ensure both Google and LLM are included for study abroad queries
if "Google" not in tools:
tools.append("Google")
if "LLM" not in tools:
tools.append("LLM")
method = PWS_Base(planner_model=model, solver_model=model,
fewshot=fewshots.STUDY_ABROAD_PWS, available_tools=tools)
else:
method = PWS_Base(planner_model=model, solver_model=model,
fewshot=fewshots.TRIVIAQA_PWS, available_tools=tools)
response = method.run(input_text)
# Extract planner log
plan = response["planner_log"].split(input_text)[1].strip('\n')
# Extract full solver log without truncating at "Now begin to solve the task"
solve = response["solver_log"].split(input_text)[1].strip('\n')
# Get the complete output
output = response["output"]
return plan, solve, output
with gr.Blocks() as iface:
gr.Markdown("# ReWOO Demo 🤗")
gr.Markdown("""
Demonstrating our recent work -- ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models.
Note that this demo is only a conceptual impression of our work, we use a zero-shot set up and not optimizing the run time.
""")
with gr.Row():
with gr.Column():
tools = gr.CheckboxGroup(['Wikipedia', 'Google', 'LLM', 'WolframAlpha', 'Calculator'], label="Tools")
model = gr.Dropdown(["text-davinci-003", "gpt-3.5-turbo"], label="Model")
input_text = gr.Textbox(lines=2, placeholder="Input Here...", label="Input")
with gr.Row():
submit_btn = gr.Button("Submit")
refresh_btn = gr.Button("🔄 Refresh Modules")
with gr.Column():
planner = gr.Textbox(lines=4, label="Planner")
solver = gr.Textbox(lines=4, label="Solver")
output = gr.Textbox(label="Output")
status = gr.Textbox(label="Status", value="Ready")
# Set up event handlers
submit_btn.click(fn=process, inputs=[tools, model, input_text], outputs=[planner, solver, output])
refresh_btn.click(fn=reload_modules, outputs=status)
input_text.submit(fn=process, inputs=[tools, model, input_text], outputs=[planner, solver, output]) # Keep Enter key functionality
# Examples
gr.Examples([
[["Wikipedia", "LLM"], "gpt-3.5-turbo", "American Callan Pinckney's eponymously named system became a best-selling (1980s-2000s) book/video franchise in what genre?"],
[['Google', 'LLM'], "gpt-3.5-turbo", "What is the recent paper ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models about?"],
[["Calculator","WolframAlpha"], "gpt-3.5-turbo", "the car can accelerate from 0 to 27.8 m/s in a time of 3.85 seconds. Determine the acceleration of this car in m/s/s."],
], inputs=[tools, model, input_text])
if __name__ == "__main__":
iface.launch() |