Update app.py

app.py

@@ -1,14 +1,13 @@
 # app.py
-# Advanced Hugging Face Space – Multi-Agent Chatbot
+# Advanced AI R&D Assistant for Hugging Face Spaces
 #
-# Developed with a forward-looking vision, inspired by Bill Gates’ drive for technological innovation.
-# This app leverages LangGraph, DeepSeek-R1 (via text-based function calling), and Agentic RAG to deliver
-# a multi-agent chatbot capable of autonomous reasoning and action.
+# This app leverages LangGraph, DeepSeek-R1 via text-based function calling, and Agentic RAG.
+# API keys are securely loaded via environment variables.
 #
 # To deploy:
-# 1. Add your API keys to the Hugging Face Space secrets (e.g., DEEPSEEK_API_KEY).
-# 2. Install dependencies via a requirements.txt file.
-# 3. Enjoy a robust and scalable AI assistant for research and development.
+# 1. Add your API key to Hugging Face Space secrets with the key DEEP_SEEK_API.
+# 2. Ensure your requirements.txt is properly configured.
+# 3. Run the app with Streamlit.
 
 import os
 import re
@@ -27,7 +26,7 @@ from langgraph.graph import END, StateGraph, START
 from langgraph.prebuilt import ToolNode
 from langgraph.graph.message import add_messages
 
-# Configure logging for better observability
+# Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
@@ -49,9 +48,7 @@ splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)
 research_docs = splitter.create_documents(research_texts)
 development_docs = splitter.create_documents(development_texts)
 
-embeddings = OpenAIEmbeddings(
-    model="text-embedding-3-large"
-)
+embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
 
 research_vectorstore = Chroma.from_documents(
     documents=research_docs,
@@ -99,7 +96,7 @@ Otherwise, just answer directly.
 """
     headers = {
         "Accept": "application/json",
-        "Authorization": f"Bearer {os.environ.get('DEEPSEEK_API_KEY')}",
+        "Authorization": f"Bearer {os.environ.get('DEEP_SEEK_API')}",
         "Content-Type": "application/json"
     }
     data = {
@@ -108,7 +105,6 @@ Otherwise, just answer directly.
         "temperature": 0.7,
         "max_tokens": 1024
     }
-
     response = requests.post(
         "https://api.deepseek.com/v1/chat/completions",
         headers=headers,
@@ -151,7 +147,7 @@ def generate(state: AgentState):
         docs = last_message.content[last_message.content.find("Results: ["):]
     headers = {
         "Accept": "application/json",
-        "Authorization": f"Bearer {os.environ.get('DEEPSEEK_API_KEY')}",
+        "Authorization": f"Bearer {os.environ.get('DEEP_SEEK_API')}",
         "Content-Type": "application/json"
     }
     prompt = f"""Based on these research documents, summarize the latest advancements in AI:
@@ -184,7 +180,7 @@ def rewrite(state: AgentState):
     original_question = state["messages"][0].content if state["messages"] else "N/A"
     headers = {
         "Accept": "application/json",
-        "Authorization": f"Bearer {os.environ.get('DEEPSEEK_API_KEY')}",
+        "Authorization": f"Bearer {os.environ.get('DEEP_SEEK_API')}",
         "Content-Type": "application/json"
     }
     data = {
@@ -214,7 +210,7 @@ def custom_tools_condition(state: AgentState):
         return "tools"
     return END
 
-# Build the workflow using LangGraph's StateGraph
+# Build the workflow with LangGraph's StateGraph
 workflow = StateGraph(AgentState)
 workflow.add_node("agent", agent)
 retrieve_node = ToolNode(tools)