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import json
import os
from typing import Dict
import gradio as gr
from openai import OpenAI
from opensearchpy import OpenSearch
host = "localhost"
port = 9200
OPENSEARCH_ADMIN_PASSWORD = os.getenv("OPENSEARCH_ADMIN_PASSWORD", "yw7L5u9nLs3a")
auth = (
"admin",
OPENSEARCH_ADMIN_PASSWORD,
) # For testing only. Don't store credentials in code.
# Create the client with SSL/TLS enabled, but hostname verification disabled.
os_client = OpenSearch(
hosts=[{"host": host, "port": port}],
http_compress=True, # enables gzip compression for request bodies
http_auth=auth,
use_ssl=True,
verify_certs=False,
ssl_assert_hostname=False,
ssl_show_warn=False,
)
all_props = json.load(open("all_properties.json"))
props_core = [
{
"property": prop["property"],
"address": prop["address"],
"school": prop["school"],
"listPrice": prop["listPrice"],
}
for prop in all_props
]
client = OpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_API_KEY"),
)
REQUIREMENTS_KEYS = [
"location",
"budget",
"house type",
"layout",
]
requirements: Dict[str, str] = {}
def get_requirement_prompt():
return f"Current collected requirements: {requirements}.\nPlease let me know your requirements: {[key for key in REQUIREMENTS_KEYS if key not in requirements.keys()]}"
def get_requirements(input_str):
global requirements
chat_completion = client.chat.completions.create(
messages=[
{
"role": "system",
"content": """
You are a real estate agent looking for properties for your clients. You are now extracting information to understand user's requests.
Requirements are:
* location. For example:
- City: Mountainview, San Jose, Dublin, etc
- Postal Code: 95134, etc
- Work or Regular Destination: work at Google; regular business flight
- School: a specific school name; specify a rating range for schools.
* budget. For example:
- around 1 million; no more than 1.5 million; between 800k to 1 million
* layout. For example:
- Bedroom: 3 bedrooms; no less than 2 bedrooms; single; married; 2 kids
- Bathroom: same as above
* house type (one or multiple choices). For example:
- condo, townhouse, single family
If user's input is a requirement, please provide the content in the format '{requirement_type}:{content}'. For example, 'location:Houston'. If multiple requirements are provided, separate them with |. For example, 'location:Houston|budget:1 million'.
If multiple requirements for the same type are provided, separate them with ;. For example, 'location:Houston;San Francisco'. DO NOT output multiple pairs with the same requirement type.
Otherwise please provide helpful response to the user
""",
},
{
"role": "user",
"content": input_str,
},
],
model="gpt-4o-mini",
)
output = chat_completion.choices[0].message.content
print(f"model output {output}")
message_out = ""
def set_requirement(output):
nonlocal message_out
if ":" not in output:
return
parts = output.split(":")
req = parts[0].strip()
content = output[len(req) + 1 :].strip()
if req in REQUIREMENTS_KEYS:
message_out = (
message_out
+ f"\nThanks! Collected requirement: {req}\n{get_requirement_prompt()}"
)
requirements[req] = content
if "|" in output:
all_requirements = output.split("|")
for req in all_requirements:
set_requirement(req)
else:
set_requirement(output)
return message_out.strip()
chat_history = []
def find_property(input_str):
global requirements
global chat_history
chat_completion = client.chat.completions.create(
messages=[
{
"role": "system",
"content": f"You are a real estate agent looking for properties for your clients. Here are some properties that might be of interest to you: {json.dumps(props_core)}. Client is looking for properties that meet the following requirements: {requirements}",
},
]
+ chat_history,
model="gpt-4o-mini",
)
output = chat_completion.choices[0].message.content
chat_history.append(
{
"role": "assistant",
"content": output,
}
)
return output
def process_chat_message(message, history):
global chat_history
output = ""
if len(requirements) < len(REQUIREMENTS_KEYS):
output = get_requirements(message)
if len(requirements) == len(REQUIREMENTS_KEYS):
output += "\n" + find_property(message)
else:
chat_history.append(
{
"role": "user",
"content": message,
}
)
output = find_property(message)
return output
demo = gr.ChatInterface(
fn=process_chat_message,
chatbot=gr.Chatbot(value=[[None, get_requirement_prompt()]]),
examples=[
"I want a house near Houston",
"I have two kids, what type of house would I need?",
"I have a budget of 1 million dollars",
],
title="Buyer Agent Bot",
)
demo.launch(share=True)
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