updated start chat

app.py

@@ -5,36 +5,56 @@ from langchain_anthropic import ChatAnthropic
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 
-# Load environment variables
+# Load environment variables (Hugging Face Spaces injects secrets automatically)
 load_dotenv()
 
+# Verify API key is available
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+if not ANTHROPIC_API_KEY:
+    print("WARNING: ANTHROPIC_API_KEY not found in environment variables!")
+
 
 @cl.on_chat_start
 async def on_chat_start():
     """Initialize the chain when a chat session starts."""
-    # Initialize the ChatAnthropic model with streaming enabled
-    model = ChatAnthropic(
-        model="claude-3-5-sonnet-20241022",
-        streaming=True,
-        temperature=0.7
-    )
+    # Check if API key is available
+    if not ANTHROPIC_API_KEY:
+        await cl.Message(
+            content="❌ Error: ANTHROPIC_API_KEY not configured. Please set it in Space settings → Variables and secrets."
+        ).send()
+        return
+
+    try:
+        # Initialize the ChatAnthropic model with streaming enabled
+        model = ChatAnthropic(
+            model="claude-3-5-sonnet-20241022",
+            streaming=True,
+            temperature=0.7,
+            api_key=ANTHROPIC_API_KEY  # Explicitly pass the API key
+        )
 
-    # Create the prompt template with system message for Math Tutor persona
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", "You are a friendly Math Tutor for elementary school students. Explain things simply and encouragingly. Use age-appropriate language and make learning fun!"),
-        ("human", "{input}")
-    ])
+        # Create the prompt template with system message for Math Tutor persona
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", "You are a friendly Math Tutor for elementary school students. Explain things simply and encouragingly. Use age-appropriate language and make learning fun!"),
+            ("human", "{input}")
+        ])
 
-    # Build the LCEL chain: Prompt | Model | Parser
-    chain = prompt | model | StrOutputParser()
+        # Build the LCEL chain: Prompt | Model | Parser
+        chain = prompt | model | StrOutputParser()
 
-    # Store the chain in user session
-    cl.user_session.set("chain", chain)
+        # Store the chain in user session
+        cl.user_session.set("chain", chain)
 
-    # Send welcome message
-    await cl.Message(
-        content="Hello! I'm your friendly Math Tutor. I'm here to help you learn math in a fun and easy way! What would you like to learn today? 🌟"
-    ).send()
+        # Send welcome message
+        await cl.Message(
+            content="Hello! I'm your friendly Math Tutor. I'm here to help you learn math in a fun and easy way! What would you like to learn today? 🌟"
+        ).send()
+    except Exception as e:
+        error_msg = f"❌ Error initializing chain: {str(e)}"
+        print(f"Error in on_chat_start: {error_msg}")
+        await cl.Message(
+            content=f"{error_msg}\n\nPlease check the Space logs and verify ANTHROPIC_API_KEY is set correctly."
+        ).send()
 
 
 @cl.on_message
@@ -60,6 +80,10 @@ async def on_message(message: cl.Message):
         await msg.update()
 
     except Exception as e:
+        # Log the full error for debugging
+        error_msg = f"❌ Error: {str(e)}"
+        # This will show in Hugging Face logs
+        print(f"Error in on_message: {error_msg}")
         await cl.Message(
-            content=f"Sorry, I encountered an error: {str(e)}. Please try again!"
+            content=f"{error_msg}\n\nPlease check the Space logs for more details."
         ).send()