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Merge pull request #873 from TheTopDeveloper/community-contributions-branch

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Add Week 6 finetuning solution with pickle data and enhanced modules- Joshua Oluoch (Gen AI Bootcamp)
This commit is contained in:
Ed Donner
2025-10-30 21:59:07 -04:00
committed by GitHub
parent 4d4e2478e4 91c568cf63
commit f814b77817
18 changed files with 4127 additions and 0 deletions
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week8/community_contributions/ensemble-joshua/agents/agent.py Normal file
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import logging
class Agent:
"""
An abstract superclass for Agents
Used to log messages in a way that can identify each Agent
"""
# Foreground colors
RED = '\033[31m'
GREEN = '\033[32m'
YELLOW = '\033[33m'
BLUE = '\033[34m'
MAGENTA = '\033[35m'
CYAN = '\033[36m'
WHITE = '\033[37m'
# Background color
BG_BLACK = '\033[40m'
# Reset code to return to default color
RESET = '\033[0m'
name: str = ""
color: str = '\033[37m'
def log(self, message):
"""
Log this as an info message, identifying the agent
"""
color_code = self.BG_BLACK + self.color
message = f"[{self.name}] {message}"
logging.info(color_code + message + self.RESET)

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week8/community_contributions/ensemble-joshua/agents/deals.py Normal file
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from pydantic import BaseModel
from typing import List, Dict, Self
from bs4 import BeautifulSoup
import re
import feedparser
from tqdm import tqdm
import requests
import time
feeds = [
"https://www.dealnews.com/c142/Electronics/?rss=1",
"https://www.dealnews.com/c39/Computers/?rss=1",
"https://www.dealnews.com/c238/Automotive/?rss=1",
"https://www.dealnews.com/f1912/Smart-Home/?rss=1",
"https://www.dealnews.com/c196/Home-Garden/?rss=1",
]
def extract(html_snippet: str) -> str:
"""
Use Beautiful Soup to clean up this HTML snippet and extract useful text
"""
soup = BeautifulSoup(html_snippet, 'html.parser')
snippet_div = soup.find('div', class_='snippet summary')
if snippet_div:
description = snippet_div.get_text(strip=True)
description = BeautifulSoup(description, 'html.parser').get_text()
description = re.sub('<[^<]+?>', '', description)
result = description.strip()
else:
result = html_snippet
return result.replace('\n', ' ')
class ScrapedDeal:
"""
A class to represent a Deal retrieved from an RSS feed
"""
category: str
title: str
summary: str
url: str
details: str
features: str
def __init__(self, entry: Dict[str, str]):
"""
Populate this instance based on the provided dict
"""
self.title = entry['title']
self.summary = extract(entry['summary'])
self.url = entry['links'][0]['href']
stuff = requests.get(self.url).content
soup = BeautifulSoup(stuff, 'html.parser')
content = soup.find('div', class_='content-section').get_text()
content = content.replace('\nmore', '').replace('\n', ' ')
if "Features" in content:
self.details, self.features = content.split("Features")
else:
self.details = content
self.features = ""
def __repr__(self):
"""
Return a string to describe this deal
"""
return f"<{self.title}>"
def describe(self):
"""
Return a longer string to describe this deal for use in calling a model
"""
return f"Title: {self.title}\nDetails: {self.details.strip()}\nFeatures: {self.features.strip()}\nURL: {self.url}"
@classmethod
def fetch(cls, show_progress : bool = False) -> List[Self]:
"""
Retrieve all deals from the selected RSS feeds
"""
deals = []
feed_iter = tqdm(feeds) if show_progress else feeds
for feed_url in feed_iter:
feed = feedparser.parse(feed_url)
for entry in feed.entries[:10]:
deals.append(cls(entry))
time.sleep(0.5)
return deals
class Deal(BaseModel):
"""
A class to Represent a Deal with a summary description
"""
product_description: str
price: float
url: str
class DealSelection(BaseModel):
"""
A class to Represent a list of Deals
"""
deals: List[Deal]
class Opportunity(BaseModel):
"""
A class to represent a possible opportunity: a Deal where we estimate
it should cost more than it's being offered
"""
deal: Deal
estimate: float
discount: float

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week8/community_contributions/ensemble-joshua/agents/ensemble_agent.py Normal file
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import pandas as pd
from sklearn.linear_model import LinearRegression
import joblib
import os
from agents.agent import Agent
from agents.specialist_agent import SpecialistAgent
from agents.frontier_agent import FrontierAgent
from agents.random_forest_agent import RandomForestAgent
class EnsembleAgent(Agent):
name = "Ensemble Agent"
color = Agent.YELLOW
def __init__(self, collection):
"""
Create an instance of Ensemble, by creating each of the models
And loading the weights of the Ensemble
"""
self.log("Initializing Ensemble Agent")
self.specialist = SpecialistAgent()
self.frontier = FrontierAgent(collection)
self.random_forest = RandomForestAgent()
# Resolve model path: prefer local contribution folder copy, fallback to week8 root
candidate_paths = [
os.path.join(os.path.dirname(os.path.dirname(__file__)), 'ensemble_model.pkl'), # ../../ensemble_model.pkl
os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'ensemble_model.pkl'), # ../../../ensemble_model.pkl (week8 root)
'ensemble_model.pkl',
]
model_path = next((p for p in candidate_paths if os.path.exists(p)), candidate_paths[-1])
self.model = joblib.load(model_path)
self.log("Ensemble Agent is ready")
def price(self, description: str) -> float:
"""
Run this ensemble model
Ask each of the models to price the product
Then use the Linear Regression model to return the weighted price
:param description: the description of a product
:return: an estimate of its price
"""
self.log("Running Ensemble Agent - collaborating with specialist, frontier and random forest agents")
specialist = self.specialist.price(description)
frontier = self.frontier.price(description)
random_forest = self.random_forest.price(description)
X = pd.DataFrame({
'Specialist': [specialist],
'Frontier': [frontier],
'RandomForest': [random_forest],
'Min': [min(specialist, frontier, random_forest)],
'Max': [max(specialist, frontier, random_forest)],
})
y = max(0, self.model.predict(X)[0])
self.log(f"Ensemble Agent complete - returning ${y:.2f}")
return y

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week8/community_contributions/ensemble-joshua/agents/messaging_agent.py Normal file
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import os
# from twilio.rest import Client
from agents.deals import Opportunity
import http.client
import urllib
from agents.agent import Agent
# Uncomment the Twilio lines if you wish to use Twilio
DO_TEXT = False
DO_PUSH = True
class MessagingAgent(Agent):
name = "Messaging Agent"
color = Agent.WHITE
def __init__(self):
"""
Set up this object to either do push notifications via Pushover,
or SMS via Twilio,
whichever is specified in the constants
"""
self.log(f"Messaging Agent is initializing")
if DO_TEXT:
account_sid = os.getenv('TWILIO_ACCOUNT_SID', 'your-sid-if-not-using-env')
auth_token = os.getenv('TWILIO_AUTH_TOKEN', 'your-auth-if-not-using-env')
self.me_from = os.getenv('TWILIO_FROM', 'your-phone-number-if-not-using-env')
self.me_to = os.getenv('MY_PHONE_NUMBER', 'your-phone-number-if-not-using-env')
# self.client = Client(account_sid, auth_token)
self.log("Messaging Agent has initialized Twilio")
if DO_PUSH:
self.pushover_user = os.getenv('PUSHOVER_USER', 'your-pushover-user-if-not-using-env')
self.pushover_token = os.getenv('PUSHOVER_TOKEN', 'your-pushover-user-if-not-using-env')
self.log("Messaging Agent has initialized Pushover")
def message(self, text):
"""
Send an SMS message using the Twilio API
"""
self.log("Messaging Agent is sending a text message")
message = self.client.messages.create(
from_=self.me_from,
body=text,
to=self.me_to
)
def push(self, text):
"""
Send a Push Notification using the Pushover API
"""
self.log("Messaging Agent is sending a push notification")
conn = http.client.HTTPSConnection("api.pushover.net:443")
conn.request("POST", "/1/messages.json",
urllib.parse.urlencode({
"token": self.pushover_token,
"user": self.pushover_user,
"message": text,
"sound": "cashregister"
}), { "Content-type": "application/x-www-form-urlencoded" })
conn.getresponse()
def alert(self, opportunity: Opportunity):
"""
Make an alert about the specified Opportunity
"""
text = f"Deal Alert! Price=${opportunity.deal.price:.2f}, "
text += f"Estimate=${opportunity.estimate:.2f}, "
text += f"Discount=${opportunity.discount:.2f} :"
text += opportunity.deal.product_description[:10]+'... '
text += opportunity.deal.url
if DO_TEXT:
self.message(text)
if DO_PUSH:
self.push(text)
self.log("Messaging Agent has completed")

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week8/community_contributions/ensemble-joshua/agents/planning_agent.py Normal file
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from typing import Optional, List
from agents.agent import Agent
from agents.deals import ScrapedDeal, DealSelection, Deal, Opportunity
from agents.scanner_agent import ScannerAgent
from agents.ensemble_agent import EnsembleAgent
from agents.messaging_agent import MessagingAgent
class PlanningAgent(Agent):
name = "Planning Agent"
color = Agent.GREEN
DEAL_THRESHOLD = 50
def __init__(self, collection):
"""
Create instances of the 3 Agents that this planner coordinates across
"""
self.log("Planning Agent is initializing")
self.scanner = ScannerAgent()
self.ensemble = EnsembleAgent(collection)
self.messenger = MessagingAgent()
self.log("Planning Agent is ready")
def run(self, deal: Deal) -> Opportunity:
"""
Run the workflow for a particular deal
:param deal: the deal, summarized from an RSS scrape
:returns: an opportunity including the discount
"""
self.log("Planning Agent is pricing up a potential deal")
estimate = self.ensemble.price(deal.product_description)
discount = estimate - deal.price
self.log(f"Planning Agent has processed a deal with discount ${discount:.2f}")
return Opportunity(deal=deal, estimate=estimate, discount=discount)
def plan(self, memory: List[str] = []) -> Optional[Opportunity]:
"""
Run the full workflow:
1. Use the ScannerAgent to find deals from RSS feeds
2. Use the EnsembleAgent to estimate them
3. Use the MessagingAgent to send a notification of deals
:param memory: a list of URLs that have been surfaced in the past
:return: an Opportunity if one was surfaced, otherwise None
"""
self.log("Planning Agent is kicking off a run")
selection = self.scanner.scan(memory=memory)
if selection:
opportunities = [self.run(deal) for deal in selection.deals[:5]]
opportunities.sort(key=lambda opp: opp.discount, reverse=True)
best = opportunities[0]
self.log(f"Planning Agent has identified the best deal has discount ${best.discount:.2f}")
if best.discount > self.DEAL_THRESHOLD:
self.messenger.alert(best)
self.log("Planning Agent has completed a run")
return best if best.discount > self.DEAL_THRESHOLD else None
return None

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week8/community_contributions/ensemble-joshua/agents/random_forest_agent.py Normal file
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# imports
import os
import re
from typing import List
from sentence_transformers import SentenceTransformer
import joblib
import os
from agents.agent import Agent
class RandomForestAgent(Agent):
name = "Random Forest Agent"
color = Agent.MAGENTA
def __init__(self):
"""
Initialize this object by loading in the saved model weights
and the SentenceTransformer vector encoding model
"""
self.log("Random Forest Agent is initializing")
self.vectorizer = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
# Resolve model path: prefer local contribution folder copy, fallback to week8 root
candidate_paths = [
os.path.join(os.path.dirname(os.path.dirname(__file__)), 'random_forest_model.pkl'), # ../../random_forest_model.pkl
os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'random_forest_model.pkl'), # ../../../random_forest_model.pkl (week8 root)
'random_forest_model.pkl',
]
model_path = next((p for p in candidate_paths if os.path.exists(p)), candidate_paths[-1])
self.model = joblib.load(model_path)
self.log("Random Forest Agent is ready")
def price(self, description: str) -> float:
"""
Use a Random Forest model to estimate the price of the described item
:param description: the product to be estimated
:return: the price as a float
"""
self.log("Random Forest Agent is starting a prediction")
vector = self.vectorizer.encode([description])
result = max(0, self.model.predict(vector)[0])
self.log(f"Random Forest Agent completed - predicting ${result:.2f}")
return result

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week8/community_contributions/ensemble-joshua/agents/scanner_agent.py Normal file
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import os
import json
from typing import Optional, List
from openai import OpenAI
from agents.deals import ScrapedDeal, DealSelection
from agents.agent import Agent
class ScannerAgent(Agent):
MODEL = "gpt-4o-mini"
SYSTEM_PROMPT = """You identify and summarize the 5 most detailed deals from a list, by selecting deals that have the most detailed, high quality description and the most clear price.
Respond strictly in JSON with no explanation, using this format. You should provide the price as a number derived from the description. If the price of a deal isn't clear, do not include that deal in your response.
Most important is that you respond with the 5 deals that have the most detailed product description with price. It's not important to mention the terms of the deal; most important is a thorough description of the product.
Be careful with products that are described as "$XXX off" or "reduced by $XXX" - this isn't the actual price of the product. Only respond with products when you are highly confident about the price.
{"deals": [
{
"product_description": "Your clearly expressed summary of the product in 4-5 sentences. Details of the item are much more important than why it's a good deal. Avoid mentioning discounts and coupons; focus on the item itself. There should be a paragpraph of text for each item you choose.",
"price": 99.99,
"url": "the url as provided"
},
...
]}"""
USER_PROMPT_PREFIX = """Respond with the most promising 5 deals from this list, selecting those which have the most detailed, high quality product description and a clear price that is greater than 0.
Respond strictly in JSON, and only JSON. You should rephrase the description to be a summary of the product itself, not the terms of the deal.
Remember to respond with a paragraph of text in the product_description field for each of the 5 items that you select.
Be careful with products that are described as "$XXX off" or "reduced by $XXX" - this isn't the actual price of the product. Only respond with products when you are highly confident about the price.
Deals:
"""
USER_PROMPT_SUFFIX = "\n\nStrictly respond in JSON and include exactly 5 deals, no more."
name = "Scanner Agent"
color = Agent.CYAN
def __init__(self):
"""
Set up this instance by initializing OpenAI
"""
self.log("Scanner Agent is initializing")
self.openai = OpenAI()
self.log("Scanner Agent is ready")
def fetch_deals(self, memory) -> List[ScrapedDeal]:
"""
Look up deals published on RSS feeds
Return any new deals that are not already in the memory provided
"""
self.log("Scanner Agent is about to fetch deals from RSS feed")
urls = [opp.deal.url for opp in memory]
scraped = ScrapedDeal.fetch()
result = [scrape for scrape in scraped if scrape.url not in urls]
self.log(f"Scanner Agent received {len(result)} deals not already scraped")
return result
def make_user_prompt(self, scraped) -> str:
"""
Create a user prompt for OpenAI based on the scraped deals provided
"""
user_prompt = self.USER_PROMPT_PREFIX
user_prompt += '\n\n'.join([scrape.describe() for scrape in scraped])
user_prompt += self.USER_PROMPT_SUFFIX
return user_prompt
def scan(self, memory: List[str]=[]) -> Optional[DealSelection]:
"""
Call OpenAI to provide a high potential list of deals with good descriptions and prices
Use StructuredOutputs to ensure it conforms to our specifications
:param memory: a list of URLs representing deals already raised
:return: a selection of good deals, or None if there aren't any
"""
scraped = self.fetch_deals(memory)
if scraped:
user_prompt = self.make_user_prompt(scraped)
self.log("Scanner Agent is calling OpenAI using Structured Output")
result = self.openai.beta.chat.completions.parse(
model=self.MODEL,
messages=[
{"role": "system", "content": self.SYSTEM_PROMPT},
{"role": "user", "content": user_prompt}
],
response_format=DealSelection
)
result = result.choices[0].message.parsed
result.deals = [deal for deal in result.deals if deal.price>0]
self.log(f"Scanner Agent received {len(result.deals)} selected deals with price>0 from OpenAI")
return result
return None

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week8/community_contributions/ensemble-joshua/api.py Normal file
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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import os
import chromadb
from agents.specialist_agent import SpecialistAgent
from agents.frontier_agent import FrontierAgent
from agents.random_forest_agent import RandomForestAgent
from agents.ensemble_agent import EnsembleAgent
from deal_agent_framework import DealAgentFramework
class PriceRequest(BaseModel):
description: str
class DealScanResponse(BaseModel):
opportunities: list
DB_PATH = os.path.join(os.path.dirname(__file__), "../../products_vectorstore")
client = chromadb.PersistentClient(path=DB_PATH)
collection = client.get_or_create_collection("products")
app = FastAPI(title="Week8 Pricer API", version="1.0.0")
@app.get("/healthz")
def healthz():
return {"ok": True}
@app.post("/price/specialist")
def price_specialist(body: PriceRequest):
if not body.description:
raise HTTPException(400, "description is required")
agent = SpecialistAgent()
price = float(agent.price(body.description))
return {"price": price, "agent": "specialist"}
@app.post("/price/frontier")
def price_frontier(body: PriceRequest):
if not body.description:
raise HTTPException(400, "description is required")
agent = FrontierAgent(collection)
price = float(agent.price(body.description))
return {"price": price, "agent": "frontier"}
@app.post("/price/random_forest")
def price_random_forest(body: PriceRequest):
if not body.description:
raise HTTPException(400, "description is required")
agent = RandomForestAgent()
price = float(agent.price(body.description))
return {"price": price, "agent": "random_forest"}
@app.post("/price/ensemble")
def price_ensemble(body: PriceRequest):
if not body.description:
raise HTTPException(400, "description is required")
agent = EnsembleAgent(collection)
price = float(agent.price(body.description))
return {"price": price, "agent": "ensemble"}
@app.post("/deals/scan")
def deals_scan():
framework = DealAgentFramework()
opportunities = framework.run()
return {"count": len(opportunities), "opportunities": [o.dict() for o in opportunities]}

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week8/community_contributions/ensemble-joshua/ensemble_model.pkl Normal file
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# imports
import os
import re
import math
import json
from typing import List, Dict
from openai import OpenAI
try:
from openai import APIStatusError
APIStatusError = Exception
import statistics
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
import chromadb
from items import Item
from testing import Tester
from agents.agent import Agent
class FrontierAgent(Agent):
name = "Frontier Agent"
color = Agent.BLUE
MODEL = "gpt-4o-mini"
def __init__(self, collection):
"""
Set up this instance by connecting to OpenAI or DeepSeek, to the Chroma Datastore,
And setting up the vector encoding model
"""
self.log("Initializing Frontier Agent")
deepseek_api_key = os.getenv("DEEPSEEK_API_KEY")
if deepseek_api_key:
self.client = OpenAI(api_key=deepseek_api_key, base_url="https://api.deepseek.com")
self.MODEL = "deepseek-chat"
self.log("Frontier Agent is set up with DeepSeek")
else:
self.client = OpenAI()
self.MODEL = "gpt-4o-mini"
self.log("Frontier Agent is setting up with OpenAI")
self.collection = collection
self.model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
self.log("Frontier Agent is ready")
def make_context(self, similars: List[str], prices: List[float]) -> str:
"""
Create context that can be inserted into the prompt
:param similars: similar products to the one being estimated
:param prices: prices of the similar products
:return: text to insert in the prompt that provides context
"""
message = "To provide some context, here are some other items that might be similar to the item you need to estimate.\n\n"
for similar, price in zip(similars, prices):
message += f"Potentially related product:\n{similar}\nPrice is ${price:.2f}\n\n"
return message
def messages_for(self, description: str, similars: List[str], prices: List[float]) -> List[Dict[str, str]]:
"""
Create the message list to be included in a call to OpenAI
With the system and user prompt
:param description: a description of the product
:param similars: similar products to this one
:param prices: prices of similar products
:return: the list of messages in the format expected by OpenAI
"""
system_message = "You estimate prices of items. Reply only with the price, no explanation"
user_prompt = self.make_context(similars, prices)
user_prompt += "And now the question for you:\n\n"
user_prompt += "How much does this cost?\n\n" + description
return [
{"role": "system", "content": system_message},
{"role": "user", "content": user_prompt},
{"role": "assistant", "content": "Price is $"}
]
def find_similars(self, description: str):
"""
Return a list of items similar to the given one by looking in the Chroma datastore
"""
self.log("Frontier Agent is performing a RAG search of the Chroma datastore to find 5 similar products")
vector = self.model.encode([description])
results = self.collection.query(query_embeddings=vector.astype(float).tolist(), n_results=5)
documents = results['documents'][0][:]
prices = [m['price'] for m in results['metadatas'][0][:]]
self.log("Frontier Agent has found similar products")
return documents, prices
def get_price(self, s) -> float:
"""
A utility that plucks a floating point number out of a string
"""
s = s.replace('$','').replace(',','')
match = re.search(r"[-+]?\d*\.\d+|\d+", s)
return float(match.group()) if match else 0.0
def price(self, description: str) -> float:
"""
Make a call to OpenAI or DeepSeek to estimate the price of the described product,
by looking up 5 similar products and including them in the prompt to give context
:param description: a description of the product
:return: an estimate of the price
"""
documents, prices = self.find_similars(description)
# If external calls are disabled, or similar pricing is empty, use heuristic
allow_external = os.getenv("FRONTIER_ALLOW_EXTERNAL", "true").lower() in {"1", "true", "yes"}
def heuristic_price() -> float:
if prices:
# Robust central tendency fallback
try:
return float(statistics.median(prices))
except Exception:
return float(sum(prices) / max(len(prices), 1))
# As a last resort, return 0.0
return 0.0
if not allow_external:
self.log("External LLM calls disabled via FRONTIER_ALLOW_EXTERNAL; using heuristic fallback")
result = heuristic_price()
self.log(f"Frontier Agent (fallback) - predicting ${result:.2f}")
return result
self.log(f"Frontier Agent is about to call {self.MODEL} with context including 5 similar products")
try:
response = self.client.chat.completions.create(
model=self.MODEL,
messages=self.messages_for(description, documents, prices),
seed=42,
max_tokens=5,
)
reply = response.choices[0].message.content
result = self.get_price(reply)
self.log(f"Frontier Agent completed - predicting ${result:.2f}")
return result
except APIStatusError as e: # Insufficient balance or other HTTP errors
msg = getattr(e, "message", str(e))
self.log(f"Frontier Agent API error: {msg}. Falling back to heuristic price.")
result = heuristic_price()
self.log(f"Frontier Agent (fallback) - predicting ${result:.2f}")
return result
except Exception as e:
self.log(f"Frontier Agent unexpected error: {e}. Falling back to heuristic price.")
result = heuristic_price()
self.log(f"Frontier Agent (fallback) - predicting ${result:.2f}")
return result

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week8/community_contributions/ensemble-joshua/pricer_service2.py Normal file
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import modal
from modal import App, Volume, Image
app = modal.App("pricer-service")
image = Image.debian_slim().pip_install("huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft")
secrets = [modal.Secret.from_name("hf-secret")]
# Constants
GPU = "T4"
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
PROJECT_NAME = "pricer"
HF_USER = "ed-donner"
RUN_NAME = "2024-09-13_13.04.39"
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
CACHE_DIR = "/cache"
MIN_CONTAINERS = 0
QUESTION = "How much does this cost to the nearest dollar?"
PREFIX = "Price is $"
hf_cache_volume = Volume.from_name("hf-hub-cache", create_if_missing=True)
@app.cls(
image=image.env({"HF_HUB_CACHE": CACHE_DIR}),
secrets=secrets,
gpu=GPU,
timeout=1800,
min_containers=MIN_CONTAINERS,
volumes={CACHE_DIR: hf_cache_volume}
)
class Pricer:
@modal.enter()
def setup(self):
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
from peft import PeftModel
# Quant Config
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4"
)
# Load model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer.padding_side = "right"
self.base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
quantization_config=quant_config,
device_map="auto"
)
self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_MODEL, revision=REVISION)
@modal.method()
def price(self, description: str) -> float:
import os
import re
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
from peft import PeftModel
set_seed(42)
prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
attention_mask = torch.ones(inputs.shape, device="cuda")
outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
result = self.tokenizer.decode(outputs[0])
contents = result.split("Price is $")[1]
contents = contents.replace(',','')
match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
return float(match.group()) if match else 0
# Simple HTTP endpoint so external apps can call this on Modal
@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800)
@modal.web_endpoint(method="POST")
def price_http(body: dict):
"""HTTP endpoint: {"description": str} -> {"price": float}"""
description = body.get("description", '').strip()
if not description:
return {"error": "Missing 'description'"}
pricer = Pricer()
value = pricer.price.remote(description)
return {"price": float(value)}