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# recommendations.py
import openai
from typing import List, Tuple
from utils import get_embedding
from pinecone import Pinecone
# Function to recommend products
def recommend_products(query: str, openai_api_key: str, pinecone_api_key: str, pinecone_env: str, top_k: int = 10) -> List[Tuple[str, str]]:
"""
Recommend products based on the user query.
Args:
query (str): User query.
openai_api_key (str): OpenAI API key.
pinecone_api_key (str): Pinecone API key.
pinecone_env (str): Pinecone environment.
top_k (int): Number of top recommendations to return. Default is 10.
Returns:
List[Tuple[str, str]]: List of recommended products with image URL and product name.
"""
query_embedding = get_embedding(query, openai_api_key)
if not query_embedding:
return []
try:
# Initialize Pinecone
pc = Pinecone(api_key=pinecone_api_key)
index = pc.Index("product-recommendations")
results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
recommended_products = [(match['metadata']['image_url'], f"{match['metadata']['product_name']} (Score: {match['score']})") for match in results['matches']]
return recommended_products
except Exception as e:
print(f"Error querying Pinecone: {e}")
return []
# Function to generate contextual message
def generate_contextual_message(query: str, recommendations: List[Tuple[str, str]], openai_api_key: str, system_prompt: str) -> str:
"""
Generate a contextual message based on the user query and recommendations.
Args:
query (str): User query.
recommendations (List[Tuple[str, str]]): List of recommended products.
openai_api_key (str): OpenAI API key.
system_prompt (str): System prompt for the assistant.
Returns:
str: Generated contextual message.
"""
openai.api_key = openai_api_key
product_names = [rec[1] for rec in recommendations]
prompt = f"User query: {query}\nRecommended products: {', '.join(product_names)}\n{system_prompt}"
try:
response = openai.ChatCompletion.create(
model="gpt-4", # or use "gpt-3.5-turbo" if preferred
messages=[{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
except Exception as e:
print(f"Error generating contextual message: {e}")
return "Failed to generate contextual message."
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