Update app.py

app.py

@@ -1,15 +1,20 @@
 import gradio as gr
 import cohere
 from dotenv import load_dotenv
+import os
 
 # Load environment variables
 load_dotenv()
 
-# Initialize Cohere API client
-co = cohere.Client()
+# Initialize Cohere API client with error handling for missing API key
+COHERE_API_KEY = os.getenv("COHERE_API_KEY")
+if COHERE_API_KEY is None:
+    raise ValueError("Cohere API key not found. Please set COHERE_API_KEY in environment variables.")
+co = cohere.Client(COHERE_API_KEY)
+
 MODEL_NAME = 'command-r-plus'  # Define model name as a constant
 
-# Adaptive learning functions remain the same
+# Adaptive learning functions
 def assess_knowledge(name, experience, goals):
     try:
         level_prompt = f"User Name: {name}, Experience: {experience}, Goals: {goals}. Classify as beginner, intermediate, or advanced."
@@ -17,7 +22,7 @@ def assess_knowledge(name, experience, goals):
         level = response.generations[0].text.strip()
         return level
     except Exception as e:
-        return "Error in knowledge assessment: " + str(e)
+        return f"Error in knowledge assessment: {str(e)}"
 
 def generate_explanation(topic, level):
     try:
@@ -26,7 +31,7 @@ def generate_explanation(topic, level):
         explanation = response.generations[0].text.strip()
         return explanation
     except Exception as e:
-        return "Error in generating explanation: " + str(e)
+        return f"Error in generating explanation: {str(e)}"
 
 def generate_challenge(topic, level):
     try:
@@ -35,15 +40,14 @@ def generate_challenge(topic, level):
         challenge = response.generations[0].text.strip()
         return challenge
     except Exception as e:
-        return "Error in generating challenge: " + str(e)
+        return f"Error in generating challenge: {str(e)}"
 
-# Modified tutor_interface to work with loading state
+# Tutor interface function with error handling and formatted output
 def tutor_interface(name, experience, goals, topic, request_challenge=False):
-    # Validate inputs
     if not all([name, experience, goals, topic]):
         return "", "Please fill in all required fields."
 
-    # Generate adaptive content
+    # Generate adaptive content with structured response formatting
     level = assess_knowledge(name, experience, goals)
     explanation = generate_explanation(topic, level)
 
@@ -52,10 +56,10 @@ def tutor_interface(name, experience, goals, topic, request_challenge=False):
         response = f"**Level:** {level}\n\n**Explanation:**\n{explanation}\n\n**Challenge:**\n{challenge}"
     else:
         response = f"**Level:** {level}\n\n**Explanation:**\n{explanation}"
-    
+
     return "Generation complete!", response
 
-# Gradio UI setup with theme and structured layout
+# Gradio UI setup with a loading status and structured layout
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
         # Adaptive Computer Science Tutor
@@ -91,7 +95,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     # Handle submission with loading state
     submit_button.click(
-        fn=lambda: ("Generating response...", ""),  # Clear output and show loading message
+        fn=lambda: ("Generating response...", ""),  # Show loading message
         outputs=[status, output],
     ).then(
         fn=tutor_interface,