Adding web UI using Replit

.replit

@@ -0,0 +1,12 @@
+modules = ["python-3.12"]
+run = "python main.py"
+
+[nix]
+channel = "stable-24_05"
+
+[deployment]
+run = ["sh", "-c", "python main.py"]
+
+[[ports]]
+localPort = 3000
+externalPort = 80

Pasted-Press-CTRL-C-to-quit-172-31-196-3-17-Dec-2024-14-39-55-GET-HTTP-1-1-200-2024-12-17-14-1734446468640.txt

@@ -0,0 +1,36 @@
+Press CTRL+C to quit
+172.31.196.3 - - [17/Dec/2024 14:39:55] "GET / HTTP/1.1" 200 -
+[2024-12-17 14:40:09,391] ERROR in app: Exception on /chat [POST]
+Traceback (most recent call last):
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/flask/app.py", line 1511, in wsgi_app
+    response = self.full_dispatch_request()
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/flask/app.py", line 919, in full_dispatch_request
+    rv = self.handle_user_exception(e)
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/flask/app.py", line 917, in full_dispatch_request
+    rv = self.dispatch_request()
+         ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/flask/app.py", line 902, in dispatch_request
+    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)  # type: ignore[no-any-return]
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/main.py", line 22, in chat
+    response = agent.utterance(history + [["User", message]])
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/genagents.py", line 135, in utterance
+    ret = utterance(self, curr_dialogue, context)
+          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/interaction.py", line 185, in utterance
+    return run_gpt_generate_utterance(
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/interaction.py", line 170, in run_gpt_generate_utterance
+    output, prompt, prompt_input, fail_safe = chat_safe_generate(
+                                              ^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/simulation_engine/gpt_structure.py", line 143, in chat_safe_generate
+    response = func_clean_up(response, prompt=prompt)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/interaction.py", line 159, in _func_clean_up
+    utterance = extract_first_json_dict(gpt_response)["utterance"]
+                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
+TypeError: 'NoneType' object is not subscriptable
+172.31.196.3 - - [17/Dec/2024 14:40:09] "POST /chat HTTP/1.1" 500 -

Pasted-Traceback-most-recent-call-last-File-home-runner-genagentsVishwas-main-py-line-35-in-mod-1734446019424.txt

@@ -0,0 +1,40 @@
+Traceback (most recent call last):
+  File "/home/runner/genagentsVishwas/main.py", line 35, in <module>
+    conversation.start()
+  File "/home/runner/genagentsVishwas/main.py", line 23, in start
+    agent_response = self.agent.utterance(self.conversation_history)
+                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/genagents.py", line 135, in utterance
+    ret = utterance(self, curr_dialogue, context)
+          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/interaction.py", line 184, in utterance
+    agent_desc = _utterance_agent_desc(agent, anchor)
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/interaction.py", line 36, in _utterance_agent_desc
+    retrieved = agent.memory_stream.retrieve([anchor], 0, n_count=120)
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/memory_stream.py", line 387, in retrieve
+    x = extract_relevance(curr_nodes, self.embeddings, focal_pt)
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/genagents/modules/memory_stream.py", line 265, in extract_relevance
+    focal_embedding = get_text_embedding(focal_pt)
+                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/simulation_engine/gpt_structure.py", line 162, in get_text_embedding
+    response = openai.embeddings.create(
+               ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/openai/_utils/_proxy.py", line 25, in __getattr__
+    proxied = self.__get_proxied__()
+              ^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/openai/_utils/_proxy.py", line 61, in __get_proxied__
+    return self.__load__()
+           ^^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/openai/_module_client.py", line 54, in __load__
+    return _load_client().embeddings
+           ^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/openai/__init__.py", line 305, in _load_client
+    _client = _ModuleClient(
+              ^^^^^^^^^^^^^^
+  File "/home/runner/genagentsVishwas/.pythonlibs/lib/python3.12/site-packages/openai/_client.py", line 92, in __init__
+    raise OpenAIError(
+openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable
+

README.md

@@ -219,6 +219,10 @@ print(response["responses"])
 
 Due to participant privacy concerns, the full agent bank containing over 1,000 generative agents based on real interviews is not publicly available at the moment. However, we plan to make aggregated responses on fixed tasks accessible for general research use in the coming months. Researchers interested in accessing individual responses on open tasks can request restricted access by contacting the authors and following a review process that ensures ethical considerations are met.
 
+## Using Web UI
+
+If you prefer to use web UI please take a look at latest changes which were made using Replit. You should be able to import the GIT repository in replit and deploy to see web UI.
+
 ## Contributing
 
 We welcome contributions to enhance the functionality and usability of this project. If you are interested in contributing, please follow these steps:
@@ -262,4 +266,4 @@ In addition, we thank Douglas Guilbeault, Amir Goldberg, Diyi Yang, Jeff Hancock
 
 For questions or inquiries, please contact the corresponding author:
 
-- **Joon Sung Park**: [joonspk@stanford.edu](mailto:joonspk@stanford.edu)
+- **Joon Sung Park**: [joonspk@stanford.edu](mailto:joonspk@stanford.edu)

agent_bank/populations/single_agent/55e2c23f-d3a7-4bed-a783-8e5072ac171c/memory_stream/nodes.json

@@ -0,0 +1,34 @@
+[
+  {
+    "node_id": 0,
+    "node_type": "observation",
+    "content": "Is from wealthy family. Family owned multiple properties in India",
+    "importance": 75,
+    "created": 1,
+    "last_retrieved": 1,
+    "pointer_id": null
+  },
+  {
+    "node_id": 1,
+    "node_type": "reflection",
+    "content": "Growing up in a wealthy family, I was surrounded by a sense of privilege that set the tone for my childhood. With multiple properties in India, my early years were filled with the comfort and security that wealth can provide. However, I often wonder how this environment shaped my understanding of relationships and the world around me. While I had access to opportunities that many could only dream of, I sometimes felt isolated from the realities faced by others. My childhood was a blend of luxury and introspection, and I find myself reflecting on how these experiences influenced my values and aspirations.",
+    "importance": 75,
+    "created": 1,
+    "last_retrieved": 1,
+    "pointer_id": [
+      0
+    ]
+  },
+  {
+    "node_id": 2,
+    "node_type": "reflection",
+    "content": "Growing up in a wealthy family provided me with numerous opportunities and resources that shaped my perspective on career choices. I often grapple with the privilege I had, which allowed me to explore various paths without the same pressures that many face. However, this privilege also made me acutely aware of the disparities in the world, pushing me to consider how I can leverage my advantages to make a meaningful impact. I aspire to build a career that not only fulfills my ambitions but also contributes positively to society, bridging the gap between my fortunate upbringing and the realities that others endure.",
+    "importance": 85,
+    "created": 1,
+    "last_retrieved": 1,
+    "pointer_id": [
+      1,
+      0
+    ]
+  }
+]

agent_bank/populations/single_agent/55e2c23f-d3a7-4bed-a783-8e5072ac171c/meta.json

@@ -0,0 +1,3 @@
+{
+  "id": "5b5423c8-2dd4-402b-aa34-53c33dc0cb12"
+}

agent_bank/populations/single_agent/55e2c23f-d3a7-4bed-a783-8e5072ac171c/scratch.json

@@ -0,0 +1,9 @@
+{
+  "first_name": "Rajan",
+  "last_name": "Shetty",
+  "age": 60,
+  "sex": "Male",
+  "race": "Asian",
+  "occupation": "Venture capitalist with 10 Billion net worth. Stanford Graduate. IIT Madras graduate. ",
+  "political_views": "Moderate"
+}

agent_bank/populations/single_agent/software_engineer/memory_stream/embeddings.json

@@ -0,0 +1,2 @@
+
+{}

agent_bank/populations/single_agent/software_engineer/memory_stream/nodes.json

@@ -0,0 +1,30 @@
+
+[
+  {
+    "node_id": 0,
+    "node_type": "observation",
+    "content": "I lead a team of 6 engineers working on cloud infrastructure and distributed systems. We focus on scalability and reliability of our core services.",
+    "importance": 85,
+    "created": 1,
+    "last_retrieved": 1,
+    "pointer_id": null
+  },
+  {
+    "node_id": 1,
+    "node_type": "observation",
+    "content": "Recently completed a major project migrating our monolithic architecture to microservices, which improved system performance by 40%.",
+    "importance": 85,
+    "created": 2,
+    "last_retrieved": 2,
+    "pointer_id": null
+  },
+  {
+    "node_id": 2,
+    "node_type": "observation",
+    "content": "I enjoy mentoring junior developers and conducting technical design reviews. Collaboration and knowledge sharing are important parts of our team culture.",
+    "importance": 75,
+    "created": 3,
+    "last_retrieved": 3,
+    "pointer_id": null
+  }
+]

agent_bank/populations/single_agent/software_engineer/meta.json

@@ -0,0 +1,4 @@
+
+{
+  "id": "software-engineer-001"
+}

agent_bank/populations/single_agent/software_engineer/scratch.json

@@ -0,0 +1,15 @@
+
+{
+  "first_name": "Alex",
+  "last_name": "Chen",
+  "age": 32,
+  "occupation": "Senior Software Engineer",
+  "company": "Fortune 500 Tech Company",
+  "location": "San Francisco Bay Area",
+  "education": "MS Computer Science",
+  "years_of_experience": 8,
+  "expertise": ["Cloud Computing", "Distributed Systems", "Python", "Java", "System Design"],
+  "role": "Technical Lead",
+  "personality_traits": ["analytical", "detail-oriented", "collaborative"],
+  "interests": ["technology", "software architecture", "machine learning", "hiking", "photography"]
+}

db/db.py

@@ -0,0 +1,98 @@
+
+import os
+import json
+import psycopg2
+from psycopg2.extras import Json
+from pathlib import Path
+
+def get_db_connection():
+    return psycopg2.connect(os.environ['DATABASE_URL'])
+
+def import_agents():
+    conn = get_db_connection()
+    cur = conn.cursor()
+
+    base_path = Path(__file__).parent.parent / 'agent_bank/populations/gss_agents'
+
+    for agent_dir in base_path.iterdir():
+        if agent_dir.is_dir():
+            meta_file = agent_dir / 'meta.json'
+            scratch_file = agent_dir / 'scratch.json'
+
+            if meta_file.exists() and scratch_file.exists():
+                with open(meta_file) as f:
+                    meta = json.load(f)
+                with open(scratch_file) as f:
+                    data = json.load(f)
+
+                cur.execute("""
+                    INSERT INTO agents (id, first_name, last_name, age, sex, 
+                                      ethnicity, race, political_views, education,
+                                      occupation, religion, city, state, data)
+                    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                    ON CONFLICT (id) DO UPDATE SET
+                    data = EXCLUDED.data
+                """, (
+                    meta['id'],
+                    data.get('first_name'),
+                    data.get('last_name'), 
+                    data.get('age'),
+                    data.get('sex'),
+                    data.get('ethnicity'),
+                    data.get('race'),
+                    data.get('political_views'),
+                    data.get('highest_degree_received'),
+                    data.get('work_status'),
+                    data.get('religion'),
+                    data.get('city'),
+                    data.get('state'),
+                    Json(data)
+                ))
+
+    conn.commit()
+    cur.close()
+    conn.close()
+
+def search_agents(**criteria):
+    conn = get_db_connection()
+    cur = conn.cursor()
+
+    query = "SELECT id, first_name, last_name, age, sex, race, occupation, religion, data FROM agents WHERE 1=1"
+    params = []
+
+    if 'age' in criteria:
+        query += " AND age = %s"
+        params.append(int(criteria['age']))
+    if 'sex' in criteria:
+        query += " AND LOWER(sex) = LOWER(%s)"
+        params.append(str(criteria['sex']))
+    if 'race' in criteria:
+        query += " AND LOWER(race) = LOWER(%s)"
+        params.append(str(criteria['race']))
+    if 'religion' in criteria:
+        query += " AND LOWER(religion) = LOWER(%s)"
+        params.append(str(criteria['religion']))
+    if 'occupation' in criteria:
+        query += " AND LOWER(occupation) = LOWER(%s)"
+        params.append(str(criteria['occupation']))
+
+    cur.execute(query, params)
+    columns = [desc[0] for desc in cur.description]
+    results = [dict(zip(columns, row)) for row in cur.fetchall()]
+    cur.close()
+    conn.close()
+    return results
+
+def search_agents_dialogue(age=None, sex=None, race=None, religion=None, occupation=None):
+    criteria = {}
+    if age:
+        criteria['age'] = age
+    if sex:
+        criteria['sex'] = sex
+    if race:
+        criteria['race'] = race  
+    if religion:
+        criteria['religion'] = religion
+    if occupation:
+        criteria['occupation'] = occupation
+    return search_agents(**criteria)

db/schema.sql

@@ -0,0 +1,26 @@
+
+CREATE TABLE agents (
+    id UUID PRIMARY KEY,
+    first_name VARCHAR(100),
+    last_name VARCHAR(100),
+    age INTEGER,
+    sex VARCHAR(20),
+    ethnicity VARCHAR(100),
+    race VARCHAR(100),
+    political_views VARCHAR(100),
+    education VARCHAR(100),
+    occupation VARCHAR(100),
+    religion VARCHAR(100),
+    city VARCHAR(100),
+    state VARCHAR(2),
+    data JSONB -- Store full agent data
+);
+
+CREATE INDEX idx_agents_age ON agents(age);
+CREATE INDEX idx_agents_sex ON agents(sex);
+CREATE INDEX idx_agents_ethnicity ON agents(ethnicity); 
+CREATE INDEX idx_agents_race ON agents(race);
+CREATE INDEX idx_agents_political_views ON agents(political_views);
+CREATE INDEX idx_agents_education ON agents(education);
+CREATE INDEX idx_agents_religion ON agents(religion);
+CREATE INDEX idx_agents_location ON agents(city, state);

genagents/genagents.py

@@ -115,10 +115,13 @@ def reflect(self, anchor, time_step=0):
 
     Parameters:
       anchor: str reflection anchor
+      time_step: int time step for the reflection
     Returns: 
       None
     """
-    self.memory_stream.reflect(anchor, time_step)
+    if not hasattr(self, 'memory_stream'):
+        self.memory_stream = MemoryStream([], {})
+    self.memory_stream.reflect(anchor=anchor, reflection_count=1, time_step=time_step)
 
 
   def categorical_resp(self, questions):