初始化项目，由ModelHub XC社区提供模型
Model: LangAGI-Lab/camel Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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license: gpl
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---
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# Counseling with CAMEL
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### Setup
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```
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import argparse
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import json
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import multiprocessing
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import re
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import traceback
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from abc import ABC, abstractmethod
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from pathlib import Path
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import requests
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from langchain.prompts import PromptTemplate
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from langchain_openai import OpenAI
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```
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### Define Agents
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```
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class Agent():
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def __init__(self, vLLM_server, model_id):
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self.llm = OpenAI(
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temperature=0.0,
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openai_api_key='EMPTY',
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openai_api_base=vLLM_server,
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max_tokens=512,
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model=model_id
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)
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def generate(self):
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pass
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```
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```
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class CBTAgent(Agent):
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def __init__(self, prompt, vLLM_server, model_id):
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super().__init__(vLLM_server, model_id)
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self.prompt_template = PromptTemplate(
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input_variables=[
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"client_information",
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"reason_counseling",
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'history',
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],
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template=prompt
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)
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def generate(self, client_information, reason, history):
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history_text = '\n'.join(
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[
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f"{message['role'].capitalize()}: {message['message']}"
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for message in history
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]
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)
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prompt = self.prompt_template.format(
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client_information=client_information,
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reason_counseling=reason,
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history= history_text
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)
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response = self.llm.invoke(prompt)
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try:
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cbt_technique = response.split("Counseling")[0].replace("\n", "")
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except:
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cbt_technique = None
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try:
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cbt_plan = response.split("Counseling planning:\n")[1].split("\nCBT")[0]
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except:
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cbt_plan = None
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return cbt_technique, cbt_plan
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```
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```
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class CounsleorAgent(Agent):
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def __init__(self, prompt, vLLM_server, model_id, cbt_plan):
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super().__init__(vLLM_server, model_id)
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self.cbt_plan = cbt_plan
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self.prompt_template = PromptTemplate(
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input_variables=[
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"client_information",
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"reason_counseling",
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"cbt_plan",
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"history"
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],
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template=prompt
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)
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def generate(self, client_information, reason, history):
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history_text = '\n'.join(
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[
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f"{message['role'].capitalize()}: {message['message']}"
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for message in history
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]
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)
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prompt = self.prompt_template.format(
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client_information=client_information,
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reason_counseling=reason,
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cbt_plan=self.cbt_plan,
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history=history_text,
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)
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# print(prompt)
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response = self.llm.invoke(prompt)
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# print(f"Response: {response}")
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if "'message':" in response:
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response = response.split("'message':")[1].split(", {")[0].replace("\"","").replace("]", "").replace("}", "")
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return response.split("Counselor:")[-1].replace("\n", "").replace("\\", "").replace("\"","").strip()
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```
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### Define prompt templates
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```
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RESPONSE_PROMPT="""<|start_header_id|>system<|end_header_id|>
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You are playing the role of a counselor in a psychological counseling session. Your task is to use the provided client information and counseling planning to generate the next counselor utterance in the dialogue. The goal is to create a natural and engaging response that builds on the previous conversation and aligns with the counseling plan.<|eot_id|><|start_header_id|>user<|end_header_id|>
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Client Information:
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{client_information}
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Reason for seeking counseling:
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{reason_counseling}
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Counseling planning:
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{cbt_plan}
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Counseling Dialogue:
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{history}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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"""
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```
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```
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CBT_PLAN_PROMPT="""<|start_header_id|>system<|end_header_id|>
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You are a counselor specializing in CBT techniques. Your task is to use the provided client information, and dialogue to generate an appropriate CBT technique and a detailed counseling plan.<|eot_id|><|start_header_id|>user<|end_header_id|>
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Types of CBT Techniques:
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Efficiency Evaluation, Pie Chart Technique, Alternative Perspective, Decatastrophizing, Pros and Cons Analysis, Evidence-Based Questioning, Reality Testing, Continuum Technique, Changing Rules to Wishes, Behavior Experiment, Problem-Solving Skills Training, Systematic Exposure
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Client Information:
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{client_information}
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Reason for seeking counseling:
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{reason_counseling}
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Counseling Dialogue:
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{history}
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Choose an appropriate CBT technique and create a counseling plan based on that technique.<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
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```
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### Start!
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```
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def collect_info(name, age, gender, occupation, education, matrital_status, family_details, reason):
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CLINET_INFO = f"""Name: {name}
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Age: {age}
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Gender: {gender}
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Occupation: {occupation}
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Education: {education}
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Marital Status: {matrital_status}
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Family Details: {family_details}"""
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REASON_FOR_COUNSELING = reason
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HISTORY_INIT = f"Counselor: Hi {name}, it's nice to meet you. How can I assist you today?\nClient: "
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return CLINET_INFO, REASON_FOR_COUNSELING, HISTORY_INIT
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def start_demo(intake_form, reason, history_init):
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model_id = "DLI-Lab/camel"
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vLLM_server = ```YOUR vLLM SERVER```
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max_turns = 20
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print("Welcome to the Multi-Turn ClientAgent Demo!\n")
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print(f"[Intake Form]")
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print(intake_form)
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print("Type 'exit' to quit the demo.\n")
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print("====== Counseling Session ======\n")
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first_response = history_init.split('Counselor: ')[-1].split('\nClient')[0]
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print(f"Counselor: {first_response}")
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num_turn = 0
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while num_turn < max_turns:
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if num_turn == 0:
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user_input = input("You (Client): ")
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# print(f"You (Client): {user_input}")
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history_init = history_init + user_input
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history = [
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{"role": "Counselor", "message": history_init.split("Counselor: ")[-1].split("\nClient")[0]},
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{"role": "Client", "message": history_init.split("Client: ")[-1]}
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]
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# print("CBT Planning")
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CBT_Planner = CBTAgent(CBT_PLAN_PROMPT, vLLM_server, model_id)
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cbt_technique, cbt_plan = CBT_Planner.generate(intake_form, reason, history)
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# print(f"CBT Technique: {cbt_technique}")
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# print(f"CBT Plan: {cbt_plan}")
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num_turn+=1
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else:
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counselor = CounsleorAgent(RESPONSE_PROMPT, vLLM_server, model_id, cbt_plan)
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counselor_response = counselor.generate(intake_form, reason, history)
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print(f"Counselor: {counselor_response}")
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history.append({"role": "Counselor", "message": counselor_response})
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user_input = input("You (Client): ")
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if user_input.lower() == 'exit':
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print("\n====== Exiting the demo. Goodbye! ======\n")
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break
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print(f"You (Client): {user_input}")
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history.append({"role": "Client", "message": user_input})
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num_turn+=1
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print("Demo completed.")
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return cbt_plan, history
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## Example
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# name = "Laura"
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# age = "45"
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# gender = "female"
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# occupation = "Office Job"
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# education = "College Graduate"
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# matrital_status = "Single"
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# family_details = "Lives alone"
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name = input("Let's begin the pre-counseling session. What is your name? ")
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age = input("How old are you? ")
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gender = input("What is your gender? (e.g., Male, Female)")
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occupation = input("What is your occupation? ")
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education = input("What is your highest level of education? (e.g., College Graduate)")
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marital_status = input("What is your marital status? (e.g., Single, Married)")
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family_details = input("Can you briefly describe your family situation? (e.g., Lives alone)")
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reason = input("What brings you here for counseling? Please explain briefly. ")
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CLINET_INFO, REASON_FOR_COUNSELING, HISTORY_INIT = collect_info(name, age, gender, occupation, education, matrital_status, family_details, reason)
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cbt_plan, history = start_demo(CLINET_INFO, REASON_FOR_COUNSELING, HISTORY_INIT)
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print(f"CBT Plan: {cbt_plan}\n\n")
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for message in history:
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print(f"{message['role']}: {message['message']}")
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```
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config.json
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config.json
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{
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"_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct",
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"architectures": [
|
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"LlamaForCausalLM"
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 128000,
|
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"eos_token_id": 128009,
|
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"hidden_act": "silu",
|
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 8192,
|
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"model_type": "llama",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
|
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": null,
|
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.38.2",
|
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"use_cache": true,
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"vocab_size": 128257
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}
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12
generation_config.json
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"do_sample": true,
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128009
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"max_length": 4096,
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"temperature": 0.6,
|
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"top_p": 0.9,
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"transformers_version": "4.38.2"
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}
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}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410562
tokenizer.json
Normal file
410562
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user