Update app.py

app.py

@@ -1,114 +1,86 @@
 import gradio as gr
 import cohere
+import os
 from dotenv import load_dotenv
 
+# Load environment variables
 load_dotenv()
 
-# Initialize Cohere API
-from google.colab import userdata
-
-co = cohere.Client(userdata.get('COHERE_API_KEY'))
+# Initialize Cohere API client
+co = cohere.Client()
 
 # Adaptive learning functions
 def assess_knowledge(name, experience, goals):
-    # Analyze the learner's input to classify their level
-    level_prompt = f"User Name: {name}, Experience: {experience}, Goals: {goals}. Classify as beginner, intermediate, or advanced."
-    response = co.generate(prompt=level_prompt)
-    level = response.generations[0].text.strip()
-
-    return level
+    try:
+        level_prompt = f"User Name: {name}, Experience: {experience}, Goals: {goals}. Classify as beginner, intermediate, or advanced."
+        response = co.generate(prompt=level_prompt)
+        level = response.generations[0].text.strip()
+        return level
+    except Exception as e:
+        return "Error in knowledge assessment: " + str(e)
 
 def generate_explanation(topic, level):
-    # Generate adaptive explanations based on learner level
-    explanation_prompt = f"Explain the topic '{topic}' to a {level} level student."
-    response = co.generate(prompt=explanation_prompt)
-    explanation = response.generations[0].text.strip()
-
-    return explanation
+    try:
+        explanation_prompt = f"Explain the topic '{topic}' to a {level} level student."
+        response = co.generate(prompt=explanation_prompt)
+        explanation = response.generations[0].text.strip()
+        return explanation
+    except Exception as e:
+        return "Error in generating explanation: " + str(e)
 
 def generate_challenge(topic, level):
-    # Craft a prompt that provides a tailored challenge
-    challenge_prompt = f"Generate a {level} level challenge for learning '{topic}' in computer science."
-    response = co.generate(prompt=challenge_prompt)
-    challenge = response.generations[0].text.strip()
-
-    return challenge
+    try:
+        challenge_prompt = f"Generate a {level} level challenge for learning '{topic}' in computer science."
+        response = co.generate(prompt=challenge_prompt)
+        challenge = response.generations[0].text.strip()
+        return challenge
+    except Exception as e:
+        return "Error in generating challenge: " + str(e)
 
 def tutor_interface(name, experience, goals, topic, request_challenge=False):
-    # Basic input validation
+    # Validate inputs
     if not all([name, experience, goals, topic]):
-        return "Please fill in all the required fields."
+        return "Please fill in all required fields."
 
-    # Assess knowledge and generate explanation as before
+    # Generate adaptive content
     level = assess_knowledge(name, experience, goals)
     explanation = generate_explanation(topic, level)
 
-
-    # Generate challenge if requested
     if request_challenge:
         challenge = generate_challenge(topic, level)
-        return f"Level: {level}\nExplanation:\n{explanation}\n\nChallenge:\n{challenge}"
+        return f"**Level:** {level}\n\n**Explanation:**\n{explanation}\n\n**Challenge:**\n{challenge}"
     else:
-        return f"Level: {level}\nExplanation:\n{explanation}"
-
+        return f"**Level:** {level}\n\n**Explanation:**\n{explanation}"
 
-# Gradio UI with markdown and structured layout
+# Gradio UI setup with theme and structured layout
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
+    gr.Markdown("""
         # Adaptive Computer Science Tutor
 
         Welcome to your personalized Computer Science Tutor! This tutor adapts to your level and learning pace,
         offering explanations and practice challenges in areas like data structures, algorithms, and more.
 
         ### How It Works
-        1. Enter your background and goals
-        2. Choose a topic you want to learn
-        3. Get adaptive explanations and challenges suited to your level
-        """
-    )
+        1. Enter your background and goals.
+        2. Choose a topic you want to learn.
+        3. Get adaptive explanations and challenges suited to your level.
+    """)
 
-    with gr.Group():
-        with gr.Row():
-            # User profile inputs
-            with gr.Column(scale=1):
-                name = gr.Textbox(
-                    label="Your Name",
-                    placeholder="Enter your name",
-                    # required=True
-                )
-                experience = gr.Textbox(
-                    label="Programming Experience",
-                    placeholder="e.g., Beginner, 2 years Python, etc.",
-                    # required=True
-                )
-
-            with gr.Column(scale=1):
-                goals = gr.Textbox(
-                    label="Learning Goals",
-                    placeholder="What do you want to achieve?",
-                    # required=True
-                )
-                topic = gr.Textbox(
-                    label="Topic of Interest",
-                    placeholder="e.g., Binary Search, Arrays, etc.",
-                    # required=True
-                )
-
-    with gr.Group():
-        with gr.Row():
-            request_challenge = gr.Checkbox(
-                label="Include a practice challenge",
-                value=False
-            )
-
-    # Output area
-    output = gr.Markdown(
-        min_height=50,
-        label="Tutor's Response"
-    )
+    with gr.Row():
+        with gr.Column(scale=1):
+            name = gr.Textbox(label="Your Name", placeholder="Enter your name")
+            experience = gr.Textbox(label="Programming Experience", placeholder="e.g., Beginner, 2 years Python")
+
+        with gr.Column(scale=1):
+            goals = gr.Textbox(label="Learning Goals", placeholder="What do you want to achieve?")
+            topic = gr.Textbox(label="Topic of Interest", placeholder="e.g., Binary Search, Arrays")
 
-    # Submit button
+    request_challenge = gr.Checkbox(label="Include a practice challenge", value=False)
+
+    # Output display with markdown formatting
+    output = gr.Markdown(label="Tutor's Response")
+
+    # Trigger action
     submit_button = gr.Button("Get Started", variant="primary")
     submit_button.click(
         tutor_interface,
@@ -116,7 +88,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         outputs=output
     )
 
-
 # Launch the app
 if __name__ == "__main__":
     demo.launch()