Update app.py

app.py

@@ -1,40 +1,13 @@
 import gradio as gr
 from groq import Groq
 import os
-import matplotlib.pyplot as plt
-import json
-from PIL import Image
-import requests
-from io import BytesIO
+import openai  # For integration with OpenAI's image generation API (e.g., DALL·E)
 
 # Initialize Groq client
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-def generate_visual_response(subject, topic):
-    """
-    Generate a visual response based on the subject and topic.
-    """
-    if subject == "Math":
-        # Example: Generate a simple plot for Math
-        plt.figure()
-        plt.plot([0, 1, 2, 3, 4], [0, 1, 4, 9, 16], label="y = x^2")
-        plt.title(f"Math Graph: {topic}")
-        plt.xlabel("x")
-        plt.ylabel("y")
-        plt.legend()
-        plt.savefig("math_plot.png")
-        return "math_plot.png"
-    
-    elif subject == "Science":
-        # Example: Fetch an image from Unsplash for Science
-        response = requests.get(f"https://source.unsplash.com/400x300/?{topic}")
-        img = Image.open(BytesIO(response.content))
-        img.save("science_image.png")
-        return "science_image.png"
-    
-    # Add more subjects as needed
-    else:
-        return None
+# Initialize OpenAI API client for image generation
+openai.api_key = os.environ["OPENAI_API_KEY"]
 
 def generate_tutor_output(subject, difficulty, student_input):
     prompt = f"""
@@ -53,7 +26,7 @@ def generate_tutor_output(subject, difficulty, student_input):
         messages=[
             {
                 "role": "system",
-                "content": f"You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way and giving math examples. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
+                "content": "You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way and giving math examples. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
             },
             {
                 "role": "user",
@@ -64,10 +37,16 @@ def generate_tutor_output(subject, difficulty, student_input):
         max_tokens=1000,
     )
 
-    output = completion.choices[0].message.content
-    visual_response = generate_visual_response(subject, student_input)
-    
-    return output, visual_response
+    return completion.choices[0].message.content
+
+def generate_visual_answer(prompt):
+    response = openai.Image.create(
+        prompt=prompt,
+        n=1,
+        size="1024x1024"
+    )
+    image_url = response['data'][0]['url']
+    return image_url
 
 with gr.Blocks() as demo:
     gr.Markdown("# 🎓 Your AI Tutor by Farhan")
@@ -95,32 +74,31 @@ with gr.Blocks() as demo:
             lesson_output = gr.Markdown(label="Lesson")
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
-            visual_output = gr.Image(label="Visual Explanation")
+            image_output = gr.Image(label="Visual Answer", elem_id="image-output")
     
     gr.Markdown("""
     ### How to Use
     1. Select a subject from the dropdown.
     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.
+    4. Click 'Generate Lesson' to receive a personalized lesson, question, feedback, and a visual answer.
     5. Review the AI-generated content to enhance your learning.
     6. Feel free to ask follow-up questions or explore new topics!
     """)
     
-    def process_output(output, visual_response):
+    def process_output(output, prompt):
         try:
-            parsed = json.loads(output)
-            return parsed["lesson"], parsed["question"], parsed["feedback"], visual_response
-        except json.JSONDecodeError as e:
-            print(f"Error parsing JSON: {e}")
-            print(f"API Response: {output}")
-            return "Error parsing response", "No question available", "No feedback available", None
+            parsed = eval(output)
+            visual_answer = generate_visual_answer(prompt)  # Get the image URL
+            return parsed["lesson"], parsed["question"], parsed["feedback"], visual_answer
+        except:
+            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)),
+        fn=lambda s, d, i: process_output(generate_tutor_output(s, d, i), i),
         inputs=[subject, difficulty, student_input],
-        outputs=[lesson_output, question_output, feedback_output, visual_output]
+        outputs=[lesson_output, question_output, feedback_output, image_output]
     )
 
-if _name_ == "_main_":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)