Spaces:
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initial commit
Browse files- README.md +2 -2
- app.py +93 -76
- chatbot_simulator.py +3 -3
README.md
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@@ -1,7 +1,7 @@
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---
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title: App Simulator
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emoji:
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colorFrom:
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colorTo: indigo
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sdk: gradio
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sdk_version: 4.44.1
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---
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title: App Simulator
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emoji: 🎮
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colorFrom: purple
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colorTo: indigo
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sdk: gradio
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sdk_version: 4.44.1
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app.py
CHANGED
@@ -2,84 +2,103 @@ import gradio as gr
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from chatbot_simulator import ChatbotSimulation
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from task_specific_data_population import DataPopulation
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import os
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openai_api_key = os.getenv("OPENAI_API_KEY")
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def
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max_retries = 50 # Set the maximum number of retries
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try:
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global simulation
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simulation = ChatbotSimulation(
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site_map=sitemap_data,
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page_details=page_details,
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user_state=user_state,
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task=task,
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app_name=app_name,
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log_location=f'conversation_log_{app_name}_human.txt',
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openai_api_key=openai_api_key,
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agent='human'
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)
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text = simulation.start_conversation()
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global conversation
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conversation.append({"role": "assistant", "content": text})
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log_conversation(simulation.log_location)
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global display_conversation
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display_conversation.append(('Start Simulator', text))
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if len(display_conversation) > 2:
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display_conversation.pop(0)
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return display_conversation
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except Exception as e:
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retry_count += 1
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print(f"Attempt {retry_count}/{max_retries}: An error occurred: {e}. Retrying...")
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def log_conversation(log_location):
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"""
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Append the conversation to the specified log file location.
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"""
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try:
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with open(log_location, 'a') as f: # Use 'a' for append mode
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for message in conversation:
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f.write(f"{message['role']}: {message['content']}\n\n")
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except Exception as e:
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print(f"Error logging conversation: {e}")
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def chatbot_interaction(user_input):
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"""Handle the conversation."""
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if simulation is None:
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return "Simulation is not initialized. Please start the simulator."
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try:
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# Perform one round of conversation
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response = simulation.one_conversation_round(user_input)
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global conversation
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conversation.append({"role": "user", "content": user_input})
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conversation.append({"role": "assistant", "content": response})
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log_conversation(simulation.log_location)
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display_conversation.append((user_input, response))
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return display_conversation
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except Exception as e:
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return f"An error occurred: {e}"
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# Gradio Interface
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with gr.Blocks() as demo:
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gr.Markdown("## Simulator Setup")
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task_input = gr.Textbox(label="Task", placeholder="Describe your task...")
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sitemap_input = gr.Textbox(label="Sitemap", placeholder="Enter the Hugging Face link to sitemap...")
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initialize_button = gr.Button("Initialize Simulator")
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#setup_output = gr.Textbox(label="Setup Status", interactive=False)
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chatbot = gr.Chatbot(label="Simulator Chat", height=800)
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user_message = gr.Textbox(label="Enter your message", placeholder="Type your message here...")
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submit_button = gr.Button("Send")
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# Initialize simulator and display the welcome message
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initialize_button.click(
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initialize_simulator,
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inputs=[task_input, app_name_input, sitemap_input],
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outputs=chatbot
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)
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# Handle conversation
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submit_button.click(
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chatbot_interaction,
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inputs=user_message,
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outputs=chatbot
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)
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# Launch the app
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demo.launch()
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from chatbot_simulator import ChatbotSimulation
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from task_specific_data_population import DataPopulation
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import os
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openai_api_key = os.getenv("OPENAI_API_KEY")
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class AppSimulator:
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def __init__(self, openai_api_key):
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self.simulation = None
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self.conversation = [] # Stores full conversation for logging
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self.display_conversation = [] # Stores last 2 messages for display
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self.openai_api_key = openai_api_key
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def initialize_simulator(self, task, app_name, sitemap):
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"""Initialize the simulator with retries in case of failure."""
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success = False
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retry_count = 0
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max_retries = 50
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while not success and retry_count < max_retries:
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try:
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# Process data
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data_population = DataPopulation(api_key=self.openai_api_key)
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sitemap_data, page_details, user_state = data_population.process_data(task, sitemap)
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self.simulation = ChatbotSimulation(
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site_map=sitemap_data,
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page_details=page_details,
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user_state=user_state,
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task=task,
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app_name=app_name,
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log_location=f'conversation_log_{app_name}_human.txt',
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openai_api_key=self.openai_api_key,
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agent='human'
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)
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# Start the conversation and update the logs and display
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text = self.simulation.start_conversation()
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self._log_message("assistant", text)
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self.display_conversation = [('Start Simulator', text)]
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return self.display_conversation
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except Exception as e:
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retry_count += 1
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print(f"Attempt {retry_count}/{max_retries}: An error occurred: {e}. Retrying...")
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def chatbot_interaction(self, user_input):
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"""Handle one round of conversation."""
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if self.simulation is None:
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return [("system", "Simulation is not initialized. Please start the simulator.")]
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try:
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# Get the response from the simulator
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response = self.simulation.one_conversation_round(user_input)
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# Log both user input and assistant's response
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self._log_message("user", user_input)
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self._log_message("assistant", response)
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# Update display conversation (keep last 2 entries)
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self.display_conversation.extend([(user_input, response)])
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while len(self.display_conversation) > 2:
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self.display_conversation.pop(0)
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print(len(self.display_conversation))
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return self.display_conversation
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except Exception as e:
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return [("system", f"An error occurred: {e}")]
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def _log_message(self, role, content):
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"""Log conversation messages to both memory and file."""
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self.conversation.append({"role": role, "content": content})
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self._write_log_to_file(content)
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def _write_log_to_file(self, content):
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"""Append conversation to a log file."""
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log_location = self.simulation.log_location if self.simulation else "conversation_log.txt"
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try:
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with open(log_location, 'a') as f:
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for message in self.conversation:
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f.write(f"{message['role']}: {message['content']}\n\n")
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except Exception as e:
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print(f"Error logging conversation: {e}")
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# JavaScript function to refresh theme to dark mode
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js_func = """
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function refresh() {
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const url = new URL(window.location);
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if (url.searchParams.get('__theme') !== 'dark') {
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url.searchParams.set('__theme', 'dark');
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window.location.href = url.href;
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}
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}
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"""
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simulator_app = AppSimulator(openai_api_key=openai_api_key)
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# Gradio Interface
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with gr.Blocks(js=js_func) as demo:
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gr.Markdown("## Simulator Setup")
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task_input = gr.Textbox(label="Task", placeholder="Describe your task...")
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sitemap_input = gr.Textbox(label="Sitemap", placeholder="Enter the Hugging Face link to sitemap...")
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initialize_button = gr.Button("Initialize Simulator")
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chatbot = gr.Chatbot(label="Simulator Chat", height=800)
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user_message = gr.Textbox(label="Enter your message", placeholder="Type your message here...")
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submit_button = gr.Button("Send")
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# Initialize simulator and display the welcome message
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initialize_button.click(
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simulator_app.initialize_simulator,
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inputs=[task_input, app_name_input, sitemap_input],
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outputs=chatbot
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)
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# Handle conversation interaction
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submit_button.click(
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simulator_app.chatbot_interaction,
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inputs=user_message,
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outputs=chatbot
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)
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# Launch the Gradio app
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demo.launch()
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chatbot_simulator.py
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class ChatbotSimulation:
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def __init__(self, site_map, page_details, user_state, task,
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app_name, log_location, openai_api_key, agent='human',
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max_steps=50, max_tokens=8192, buffer_tokens=
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self.sitemap = site_map
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self.page_details = page_details
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self.user_state = user_state
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response = self.client.chat.completions.create(
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model="gpt-4",
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messages=prompt,
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max_tokens=
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temperature=1.0,
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)
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return response.choices[0].message.content
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def _trim_conversation(self):
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"""Trim the conversation to keep it within the token limit."""
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while self._calculate_token_count(self.conversation) > self.max_tokens - self.buffer_tokens:
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self.conversation.pop(0) #
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def one_conversation_round(self, user_input):
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class ChatbotSimulation:
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def __init__(self, site_map, page_details, user_state, task,
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app_name, log_location, openai_api_key, agent='human',
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max_steps=50, max_tokens=8192, buffer_tokens=1000):
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self.sitemap = site_map
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self.page_details = page_details
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self.user_state = user_state
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response = self.client.chat.completions.create(
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model="gpt-4",
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messages=prompt,
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max_tokens=self.buffer_tokens, # Adjusted max_tokens if needed
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temperature=1.0,
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)
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return response.choices[0].message.content
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def _trim_conversation(self):
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"""Trim the conversation to keep it within the token limit."""
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while self._calculate_token_count(self.conversation) > self.max_tokens - (self.buffer_tokens+500):
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self.conversation.pop(0) #
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def one_conversation_round(self, user_input):
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