Update app.py

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 from groq import Groq
 import os
+import matplotlib.pyplot as plt
+import numpy as np
 
 # Initialize Groq client with your API key
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
 def generate_tutor_output(subject, difficulty, student_input):
-    # Construct the prompt for text generation
     prompt = f"""
     You are an expert tutor in {subject} at the {difficulty} level. 
     The student has provided the following input: "{student_input}"
@@ -19,7 +20,6 @@ def generate_tutor_output(subject, difficulty, student_input):
     Format your response as a JSON object with keys: "lesson", "question", "feedback"
     """
     
-    # Generate completion from the Groq API
     completion = client.chat.completions.create(
         messages=[
             {
@@ -35,9 +35,27 @@ def generate_tutor_output(subject, difficulty, student_input):
         max_tokens=1000,
     )
     
-    # Return the generated content (lesson, question, feedback)
     return completion.choices[0].message.content
 
+# Function to generate a simple graph (e.g., bar chart)
+def generate_graph():
+    # Example data
+    x = ['A', 'B', 'C', 'D']
+    y = [10, 20, 15, 25]
+    
+    fig, ax = plt.subplots()
+    ax.bar(x, y)
+    ax.set_title("Example Bar Chart")
+    ax.set_xlabel("Categories")
+    ax.set_ylabel("Values")
+    
+    # Save the plot to a file
+    plt.tight_layout()
+    plt.savefig("/tmp/bar_chart.png")  # Save to temp directory
+    plt.close(fig)
+    
+    return "/tmp/bar_chart.png"  # Return the path to the saved image
+
 # Set up the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# 🎓 Your AI Tutor")
@@ -67,6 +85,7 @@ with gr.Blocks() as demo:
             lesson_output = gr.Markdown(label="Lesson")
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
+            graph_output = gr.Image(label="Generated Graph")
     
     gr.Markdown("""
     ### How to Use
@@ -74,21 +93,23 @@ with gr.Blocks() as demo:
     2. Choose your difficulty level.
     3. Enter the topic or question you'd like to explore.
     4. Click 'Generate Lesson' to receive a personalized lesson, question, and feedback.
-    5. Review the AI-generated content to enhance your learning.
-    6. Feel free to ask follow-up questions or explore new topics!
+    5. The AI will also generate a simple bar chart as a visual representation.
+    6. Review the AI-generated content to enhance your learning.
+    7. Feel free to ask follow-up questions or explore new topics!
     """)
     
     def process_output(output):
         try:
             parsed = eval(output)  # Convert string to dictionary
-            return parsed["lesson"], parsed["question"], parsed["feedback"]
+            graph_path = generate_graph()  # Generate graph
+            return parsed["lesson"], parsed["question"], parsed["feedback"], graph_path
         except:
-            return "Error parsing output", "No question available", "No feedback available"
+            return "Error parsing output", "No question available", "No feedback available", None
     
     submit_button.click(
         fn=lambda s, d, i: process_output(generate_tutor_output(s, d, i)),
         inputs=[subject, difficulty, student_input],
-        outputs=[lesson_output, question_output, feedback_output]
+        outputs=[lesson_output, question_output, feedback_output, graph_output]
     )
 
 if __name__ == "__main__":