Update app.py

app.py

@@ -22,7 +22,7 @@ except Exception as e:
     logger.error("Failed to initialize Groq client: %s", str(e))
     raise
 
-# Load Text-to-Image Models (placeholders; adjust based on actual availability)
+# Load Text-to-Image Models (placeholders; adjust if models are unavailable)
 model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", fallback=None)
 model2 = gr.load("models/Purz/face-projection", fallback=None)
 
@@ -31,8 +31,8 @@ stop_event = threading.Event()
 
 # Function to generate tutor output (lesson, question, feedback)
 def generate_tutor_output(subject, difficulty, student_input):
-    if not subject or not difficulty or not student_input:
-        return '{"lesson": "Please provide all inputs.", "question": "", "feedback": ""}'
+    if not all([subject, difficulty, student_input]):
+        return '{"lesson": "Please fill in all fields.", "question": "", "feedback": ""}'
     
     prompt = f"""
     You are an expert tutor in {subject} at the {difficulty} level. 
@@ -96,7 +96,7 @@ def generate_images(text, selected_model):
 def generate_text_to_video(text):
     if not text:
         return "No text provided for video generation."
-
+    
     try:
         # Generate narration using Groq (text-to-speech simulation)
         narration_prompt = f"Convert this text to a natural-sounding narration: {text}"
@@ -148,26 +148,29 @@ def generate_text_to_video(text):
         logger.error("Error generating video: %s", str(e))
         return f"Error generating video: {str(e)}"
 
-# Gradio interface setup
+# Set up the Gradio interface
 with gr.Blocks(title="AI Tutor with Visuals") as demo:
-    gr.Markdown("# 🎓 Your AI Tutor with Visuals & Videos")
+    gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")
 
-    # Text-based output section
+    # Section for generating Text-based output
     with gr.Row():
         with gr.Column(scale=2):
             subject = gr.Dropdown(
                 ["Math", "Science", "History", "Literature", "Code", "AI"], 
                 label="Subject", 
+                info="Choose the subject of your lesson",
                 value="Math"
             )
             difficulty = gr.Radio(
                 ["Beginner", "Intermediate", "Advanced"], 
                 label="Difficulty Level", 
+                info="Select your proficiency level",
                 value="Beginner"
             )
             student_input = gr.Textbox(
                 placeholder="Type your query here...", 
-                label="Your Input"
+                label="Your Input", 
+                info="Enter the topic you want to learn"
             )
             submit_button_text = gr.Button("Generate Lesson & Question", variant="primary")
         
@@ -176,7 +179,7 @@ with gr.Blocks(title="AI Tutor with Visuals") as demo:
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
 
-    # Visual output section
+    # Section for generating Visual output
     with gr.Row():
         with gr.Column(scale=2):
             model_selector = gr.Radio(
@@ -195,27 +198,36 @@ with gr.Blocks(title="AI Tutor with Visuals") as demo:
 
     gr.Markdown("""
     ### How to Use
-    1. **Text Section**: Select a subject and difficulty, type your query, and click 'Generate Lesson & Question'.
-    2. **Visual Section**: Choose an image model and click 'Generate Visuals' for 3 images, or 'Generate Video with Voice' for a narrated video.
-    3. Enjoy your personalized learning experience!
+    1. **Text Section**: Select a subject and difficulty, type your query, and click 'Generate Lesson & Question' to get your personalized lesson, comprehension question, and feedback.
+    2. **Visual Section**: Select the model for image generation, then click 'Generate Visuals' to receive 3 variations of an image based on your topic. Click 'Generate Video with Voice' to create a video with narration.
+    3. Review the AI-generated content to enhance your learning experience!
     """)
 
     # Processing functions
     def process_output_text(subject, difficulty, student_input):
-        tutor_output = generate_tutor_output(subject, difficulty, student_input)
         try:
-            parsed = eval(tutor_output)  # Safely parse JSON (consider json.loads in production)
+            tutor_output = generate_tutor_output(subject, difficulty, student_input)
+            parsed = eval(tutor_output)  # Use json.loads in production for safety
             return parsed["lesson"], parsed["question"], parsed["feedback"]
         except Exception as e:
             logger.error("Error parsing tutor output: %s", str(e))
             return "Error parsing output", "No question available", "No feedback available"
 
     def process_output_visual(text, selected_model):
-        images = generate_images(text, selected_model)
-        return images[0], images[1], images[2]
+        try:
+            images = generate_images(text, selected_model)
+            return images[0], images[1], images[2]
+        except Exception as e:
+            logger.error("Error in process_output_visual: %s", str(e))
+            return None, None, None
 
     def process_output_video(text):
-        return generate_text_to_video(text)
+        try:
+            video_path = generate_text_to_video(text)
+            return video_path
+        except Exception as e:
+            logger.error("Error in process_output_video: %s", str(e))
+            return None
 
     # Button click handlers
     submit_button_text.click(
@@ -235,5 +247,4 @@ with gr.Blocks(title="AI Tutor with Visuals") as demo:
     )
 
 if __name__ == "__main__":
-    # Launch Gradio app
     demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)