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()