Update README.md

README.md

@@ -1,3 +1,249 @@
----
-license: gpl
----
+---
+license: gpl
+---
+# Counseling with CAMEL
+
+### Setup
+```
+import argparse
+import json
+import multiprocessing
+import re
+import traceback
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+import requests
+from langchain.prompts import PromptTemplate
+from langchain_openai import OpenAI
+```
+
+### Define Agents
+```
+class Agent():
+    def __init__(self, vLLM_server, model_id):
+        self.llm = OpenAI(
+            temperature=0.0,
+            openai_api_key='EMPTY',
+            openai_api_base=vLLM_server,
+            max_tokens=512,
+            model=model_id
+        )
+
+    def generate(self):
+        pass
+```
+
+```
+class CBTAgent(Agent):
+    def __init__(self, prompt, vLLM_server, model_id):
+        super().__init__(vLLM_server, model_id)
+        self.prompt_template = PromptTemplate(
+            input_variables=[
+                "client_information",
+                "reason_counseling",
+                'history',
+            ],
+            template=prompt
+        )
+
+    def generate(self, client_information, reason, history):
+        history_text = '\n'.join(
+            [
+                f"{message['role'].capitalize()}: {message['message']}"
+                for message in history
+            ]
+        )
+        prompt = self.prompt_template.format(
+            client_information=client_information,
+            reason_counseling=reason,
+            history= history_text
+        )
+        response = self.llm.invoke(prompt)
+
+        try:
+            cbt_technique = response.split("Counseling")[0].replace("\n", "")
+        except:
+            cbt_technique = None
+        try:
+            cbt_plan = response.split("Counseling planning:\n")[1].split("\nCBT")[0]
+        except:
+            cbt_plan = None
+
+        return cbt_technique, cbt_plan
+```
+
+```
+class CounsleorAgent(Agent):
+    def __init__(self,  prompt, vLLM_server, model_id, cbt_plan):
+       super().__init__(vLLM_server, model_id)
+       self.cbt_plan = cbt_plan
+       self.prompt_template = PromptTemplate(
+            input_variables=[
+                "client_information",
+                "reason_counseling",
+                "cbt_plan",
+                "history"
+            ],
+            template=prompt
+        )
+
+    def generate(self, client_information, reason, history):
+        history_text = '\n'.join(
+            [
+                f"{message['role'].capitalize()}: {message['message']}"
+                for message in history
+            ]
+        )
+        prompt = self.prompt_template.format(
+            client_information=client_information,
+            reason_counseling=reason,
+            cbt_plan=self.cbt_plan,
+            history=history_text,
+        )
+        # print(prompt)
+        response = self.llm.invoke(prompt)
+        # print(f"Response: {response}")
+
+        if "'message':" in response:
+            response = response.split("'message':")[1].split(", {")[0].replace("\"","").replace("]", "").replace("}", "")
+        return response.split("Counselor:")[-1].replace("\n", "").replace("\\", "").replace("\"","").strip()
+```
+
+### Define prompt templates
+```
+RESPONSE_PROMPT="""<|start_header_id|>system<|end_header_id|>
+
+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|>
+
+Client Information:
+{client_information}
+
+Reason for seeking counseling:
+{reason_counseling}
+
+Counseling planning:
+{cbt_plan}
+
+Counseling Dialogue:
+{history}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+"""
+```
+
+```
+CBT_PLAN_PROMPT="""<|start_header_id|>system<|end_header_id|>
+
+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|>
+
+Types of CBT Techniques:
+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
+
+Client Information:
+{client_information}
+
+Reason for seeking counseling:
+{reason_counseling}
+
+Counseling Dialogue:
+{history}
+
+Choose an appropriate CBT technique and create a counseling plan based on that technique.<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
+```
+
+### Start!
+```
+def collect_info(name, age, gender, occupation, education, matrital_status, family_details, reason):
+    CLINET_INFO = f"""Name: {name}
+Age: {age}
+Gender: {gender}
+Occupation: {occupation}
+Education: {education}
+Marital Status: {matrital_status}
+Family Details: {family_details}"""
+
+    REASON_FOR_COUNSELING = reason
+    HISTORY_INIT = f"Counselor: Hi {name}, it's nice to meet you. How can I assist you today?\nClient: "
+
+    return CLINET_INFO, REASON_FOR_COUNSELING, HISTORY_INIT
+
+def start_demo(intake_form, reason, history_init):
+    model_id = "DLI-Lab/camel"
+    vLLM_server = ```YOUR vLLM SERVER```
+    max_turns = 20
+
+    print("Welcome to the Multi-Turn ClientAgent Demo!\n")
+    print(f"[Intake Form]")
+    print(intake_form)
+    print("Type 'exit' to quit the demo.\n")
+
+    print("====== Counseling Session ======\n")
+    first_response = history_init.split('Counselor: ')[-1].split('\nClient')[0]
+    print(f"Counselor: {first_response}")
+
+    num_turn = 0
+    while num_turn < max_turns:
+        if num_turn == 0:
+            user_input = input("You (Client): ")
+            # print(f"You (Client): {user_input}")
+            history_init = history_init + user_input
+            history = [
+                {"role": "Counselor", "message": history_init.split("Counselor: ")[-1].split("\nClient")[0]},
+                {"role": "Client", "message": history_init.split("Client: ")[-1]}
+            ]
+            # print("CBT Planning")
+            CBT_Planner = CBTAgent(CBT_PLAN_PROMPT, vLLM_server, model_id)
+            cbt_technique, cbt_plan = CBT_Planner.generate(intake_form, reason, history)
+            # print(f"CBT Technique: {cbt_technique}")
+            # print(f"CBT Plan: {cbt_plan}")
+
+            num_turn+=1
+        else:
+            counselor = CounsleorAgent(RESPONSE_PROMPT, vLLM_server, model_id, cbt_plan)
+            counselor_response = counselor.generate(intake_form, reason, history)
+            print(f"Counselor: {counselor_response}")
+
+            history.append({"role": "Counselor", "message": counselor_response})
+
+            user_input = input("You (Client): ")
+
+            if user_input.lower() == 'exit':
+                print("\n====== Exiting the demo. Goodbye! ======\n")
+                break
+
+            print(f"You (Client): {user_input}")
+            history.append({"role": "Client", "message": user_input})
+
+            num_turn+=1
+
+    print("Demo completed.")
+    return cbt_plan, history
+
+
+## Example
+# name = "Laura"
+# age = "45"
+# gender = "female"
+# occupation =  "Office Job"
+# education = "College Graduate"
+# matrital_status = "Single"
+# family_details = "Lives alone"
+
+name = input("Let's begin the pre-counseling session. What is your name? ")
+age = input("How old are you? ")
+gender = input("What is your gender? (e.g., Male, Female)")
+occupation = input("What is your occupation? ")
+education = input("What is your highest level of education? (e.g., College Graduate)")
+marital_status = input("What is your marital status? (e.g., Single, Married)")
+family_details = input("Can you briefly describe your family situation? (e.g., Lives alone)")
+reason = input("What brings you here for counseling? Please explain briefly. ")
+
+
+CLINET_INFO, REASON_FOR_COUNSELING, HISTORY_INIT = collect_info(name, age, gender, occupation, education, matrital_status, family_details, reason)
+cbt_plan, history = start_demo(CLINET_INFO, REASON_FOR_COUNSELING, HISTORY_INIT)
+
+print(f"CBT Plan: {cbt_plan}\n\n")
+
+for message in history:
+    print(f"{message['role']}: {message['message']}")
+```