Create recommendations.py

recommendations.py

@@ -0,0 +1,67 @@
+# 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."