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import pandas as pd
from fastapi.encoders import jsonable_encoder
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.output_parsers import PydanticOutputParser
from models import ListingCollection
parser = PydanticOutputParser(pydantic_object=ListingCollection)
def generate_listings(llm, num_listings=5):
# Define parser
parser = PydanticOutputParser(pydantic_object=ListingCollection)
# Define diverse neighborhoods and features
neighborhoods = [
"Munich City Center", "Schwabing", "Maxvorstadt", "Glockenbachviertel", "Bogenhausen", "Haidhausen", "Sendling", "Isarvorstadt", "Laim", "Moosach"
]
property_types = [
"Modern Eco-Friendly Home", "Luxury Villa", "Family House", "Contemporary Townhouse", "Spacious Apartment", "Charming Duplex"
]
amenities_options = [
"Pool", "Garage", "Solar Panels", "Smart Home Technology", "Energy-Efficient Windows", "Large Garden", "Home Office", "Rooftop Terrace"
]
# Build prompt
prompt_template = f"""
Generate {{num_listings}} unique and richly detailed real estate listings in JSON format matching the schema:
{{format_instructions}}
Each listing must include:
- A vivid, engaging property description capturing architecture, unique features, interior design, and lifestyle.
- A neighborhood description highlighting community features like parks, cafes, schools, transport, culture, and atmosphere.
- Prices between $400,000 and $1,500,000.
- House sizes between 1,500 sqft and 4,500 sqft.
- Bedrooms between 2 and 6.
- Bathrooms between 1 and 4.5.
- Use random selections of amenities from: {', '.join(amenities_options)}.
- Neighborhoods should be selected from: {', '.join(neighborhoods)}.
- Property types should include variety: {', '.join(property_types)}.
Guidelines:
- Ensure each listing feels distinct and not repetitive.
- Vary tone and phrasing between listings.
- Maintain professionalism and realism.
Number of listings: {{num_listings}}
"""
prompt = PromptTemplate(
template=prompt_template,
input_variables=["num_listings"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
chain = LLMChain(llm=llm, prompt=prompt)
response = chain.run(num_listings=num_listings)
result = parser.parse(response)
df = pd.DataFrame(jsonable_encoder(result.listings))
df.to_csv("listings.csv", index=False)
return df
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