Upload app.py with huggingface_hub

app.py

@@ -31,6 +31,8 @@ from langchain_core.prompts import ChatPromptTemplate
 # LangChain OpenAI imports
 from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI  # OpenAI embeddings and models
 from langchain.embeddings.openai import OpenAIEmbeddings  # OpenAI embeddings for text vectors
+from langchain_openai import ChatOpenAI   # add this import
+
 
 # LlamaParse & LlamaIndex imports
 from llama_parse import LlamaParse  # Document parsing library
@@ -54,10 +56,16 @@ from datetime import datetime
 
 #====================================SETUP=====================================#
 # Fetch secrets from Hugging Face Spaces
-api_key = config.get("API_KEY")
-endpoint = config.get("OPENAI_API_BASE")
-llama_api_key = os.environ['GROQ_API_KEY']
-MEM0_api_key = os.environ['mem0']
+#api_key = config.get("API_KEY")
+#endpoint = config.get("OPENAI_API_BASE")
+#llama_api_key = os.environ['GROQ_API_KEY']
+#MEM0_api_key = os.environ['mem0']
+
+# Fetch secrets from Hugging Face Spaces
+api_key = os.environ.get("API_KEY")
+endpoint = os.environ.get("OPENAI_API_BASE")
+llama_api_key = os.environ.get("GROQ_API_KEY")
+MEM0_api_key = os.environ.get("mem0")
 
 # Initialize the OpenAI embedding function for Chroma
 embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
@@ -542,16 +550,25 @@ class NutritionBot:
         """
 
         # Initialize a memory client to store and retrieve customer interactions
-        self.memory = MemoryClient(api_key=userdata.get("mem0"))  # Complete the code to define the memory client API key
+        #self.memory = MemoryClient(api_key=userdata.get("mem0"))  # Complete the code to define the memory client API key
+        self.memory = MemoryClient(api_key=os.environ.get("mem0"))
 
-        # Initialize the OpenAI client using the provided credentials
+        # LLM client
         self.client = ChatOpenAI(
-            model_name="gpt-4o-mini",  # Specify the model to use (e.g., GPT-4 optimized version)
-            api_key=config.get("API_KEY"),  # API key for authentication
-            endpoint = config.get("OPENAI_API_BASE"),
-            temperature=0  # Controls randomness in responses; 0 ensures deterministic results
+        openai_api_base=os.environ.get("OPENAI_API_BASE"),
+        openai_api_key=os.environ.get("API_KEY"),
+        model="gpt-4o-mini",
+        temperature=0
         )
 
+        # Initialize the OpenAI client using the provided credentials
+        #self.client = ChatOpenAI(
+        #    model_name="gpt-4o-mini",  # Specify the model to use (e.g., GPT-4 optimized version)
+        #    api_key=config.get("API_KEY"),  # API key for authentication
+         #   endpoint = config.get("OPENAI_API_BASE"),
+        #     temperature=0  # Controls randomness in responses; 0 ensures deterministic results
+        #)
+
         # Define tools available to the chatbot, such as web search
         tools = [agentic_rag]