Update app.py

Revert changes

app.py

@@ -1,65 +1,58 @@
 import gradio as gr
 import cohere
-from dotenv import load_dotenv
 import os
+from dotenv import load_dotenv
 
 # Load environment variables
 load_dotenv()
 
-# 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
+# Initialize Cohere API client
+co = cohere.Client(os.getenv('COHERE_API_KEY'))
 
 # 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."
-        response = co.generate(prompt=level_prompt, model=MODEL_NAME)
+        response = co.generate(prompt=level_prompt)
         level = response.generations[0].text.strip()
         return level
     except Exception as e:
-        return f"Error in knowledge assessment: {str(e)}"
+        return "Error in knowledge assessment: " + str(e)
 
 def generate_explanation(topic, level):
     try:
         explanation_prompt = f"Explain the topic '{topic}' to a {level} level student."
-        response = co.generate(prompt=explanation_prompt, model=MODEL_NAME)
+        response = co.generate(prompt=explanation_prompt)
         explanation = response.generations[0].text.strip()
         return explanation
     except Exception as e:
-        return f"Error in generating explanation: {str(e)}"
+        return "Error in generating explanation: " + str(e)
 
 def generate_challenge(topic, level):
     try:
         challenge_prompt = f"Generate a {level} level challenge for learning '{topic}' in computer science."
-        response = co.generate(prompt=challenge_prompt, model=MODEL_NAME)
+        response = co.generate(prompt=challenge_prompt)
         challenge = response.generations[0].text.strip()
         return challenge
     except Exception as e:
-        return f"Error in generating challenge: {str(e)}"
+        return "Error in generating challenge: " + str(e)
 
-# 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."
+        return "Please fill in all required fields."
 
-    # Generate adaptive content with structured response formatting
+    # Generate adaptive content
     level = assess_knowledge(name, experience, goals)
     explanation = generate_explanation(topic, level)
 
     if request_challenge:
         challenge = generate_challenge(topic, level)
-        response = f"**Level:** {level}\n\n**Explanation:**\n{explanation}\n\n**Challenge:**\n{challenge}"
+        return 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
+        return f"**Level:** {level}\n\n**Explanation:**\n{explanation}"
 
-# Gradio UI setup with a loading status and structured layout
+# Gradio UI setup with theme and structured layout
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
         # Adaptive Computer Science Tutor
@@ -84,23 +77,15 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     request_challenge = gr.Checkbox(label="Include a practice challenge", value=False)
 
-    # Status message for loading state
-    status = gr.Markdown("", elem_id="status")
-    
     # Output display with markdown formatting
     output = gr.Markdown(label="Tutor's Response")
 
-    # Submit button
+    # Trigger action
     submit_button = gr.Button("Get Started", variant="primary")
-    
-    # Handle submission with loading state
     submit_button.click(
-        fn=lambda: ("Generating response...", ""),  # Show loading message
-        outputs=[status, output],
-    ).then(
-        fn=tutor_interface,
+        tutor_interface,
         inputs=[name, experience, goals, topic, request_challenge],
-        outputs=[status, output],
+        outputs=output
     )
 
 # Launch the app