Update app.py

app.py

@@ -1,27 +1,12 @@
 import gradio as gr
 from groq import Groq
 import os
-import requests  # For DeepAI API
 
-# Initialize Groq client
+# Initialize Groq client with your API key
 client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-# Function to generate image from DeepAI API
-def generate_visual_answer(prompt):
-    # Replace with DeepAI image generation API URL
-    url = "https://api.deepai.org/api/text2img"
-    response = requests.post(
-        url, 
-        data={'text': prompt},  # The prompt will be the text description for image generation
-        headers={'api-key': os.environ["DEEP_AI_API_KEY"]}  # Use DeepAI's API key here
-    )
-    if response.status_code == 200:
-        image_url = response.json()['output_url']  # URL for the generated image
-        return image_url
-    else:
-        return "Error generating image"
-
 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}"
@@ -33,7 +18,8 @@ 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=[
             {
@@ -45,17 +31,20 @@ def generate_tutor_output(subject, difficulty, student_input):
                 "content": prompt,
             }
         ],
-        model="mixtral-8x7b-32768",
+        model="mixtral-8x7b-32768",  # Model for text generation
         max_tokens=1000,
     )
-
+    
+    # Return the generated content (lesson, question, feedback)
     return completion.choices[0].message.content
 
+# Set up the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 Your AI Tutor by Farhan")
+    gr.Markdown("# 🎓 Your AI Tutor")
     
     with gr.Row():
         with gr.Column(scale=2):
+            # Input fields for subject, difficulty, and student input
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Literature", "Code", "AI"], 
                 label="Subject", 
@@ -74,33 +63,32 @@ with gr.Blocks() as demo:
             submit_button = gr.Button("Generate Lesson", variant="primary")
         
         with gr.Column(scale=3):
+            # Output fields for lesson, question, and feedback
             lesson_output = gr.Markdown(label="Lesson")
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
-            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, feedback, and a visual answer.
+    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!
     """)
     
-    def process_output(output, prompt):
+    def process_output(output):
         try:
-            parsed = eval(output)
-            visual_answer = generate_visual_answer(prompt)  # Get the image URL from DeepAI
-            return parsed["lesson"], parsed["question"], parsed["feedback"], visual_answer
+            parsed = eval(output)  # Convert string to dictionary
+            return parsed["lesson"], parsed["question"], parsed["feedback"]
         except:
-            return "Error parsing output", "No question available", "No feedback available", None
+            return "Error parsing output", "No question available", "No feedback available"
     
     submit_button.click(
-        fn=lambda s, d, i: process_output(generate_tutor_output(s, d, i), i),
+        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, image_output]
+        outputs=[lesson_output, question_output, feedback_output]
     )
 
 if __name__ == "__main__":