Update app.py

app.py

@@ -10,7 +10,7 @@ from moviepy.editor import TextClip, concatenate_videoclips, AudioFileClip, Colo
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Disable proxies to avoid 'proxies' argument error
+# Disable proxies to avoid previous 'proxies' error
 os.environ["HTTP_PROXY"] = ""
 os.environ["HTTPS_PROXY"] = ""
 
@@ -22,9 +22,20 @@ except Exception as e:
     logger.error("Failed to initialize Groq client: %s", str(e))
     raise
 
-# 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)
+# Load Text-to-Image Models with error handling
+try:
+    model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", fallback=None)
+    logger.info("Model 1 loaded successfully: SD3.5-Turbo-Realism-2.0-LoRA")
+except Exception as e:
+    logger.error("Failed to load Model 1: %s", str(e))
+    model1 = None  # Fallback to None if loading fails
+
+try:
+    model2 = gr.load("models/Purz/face-projection", fallback=None)
+    logger.info("Model 2 loaded successfully: face-projection")
+except Exception as e:
+    logger.error("Failed to load Model 2: %s", str(e))
+    model2 = None  # Fallback to None if loading fails
 
 # Stop event for threading (image generation)
 stop_event = threading.Event()
@@ -77,7 +88,7 @@ def generate_images(text, selected_model):
         return ["Invalid model selection."] * 3
 
     if model is None:
-        return ["Model not loaded."] * 3
+        return ["Selected model is not available."] * 3
 
     results = []
     for i in range(3):
@@ -98,7 +109,6 @@ def generate_text_to_video(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}"
         narration_response = client.chat.completions.create(
             messages=[{
@@ -113,35 +123,28 @@ def generate_text_to_video(text):
         )
         narration_text = narration_response.choices[0].message.content
 
-        # Simulate TTS with a silent audio clip (replace with real TTS API if available)
         with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_audio:
             audio_duration = len(narration_text.split()) / 2  # Rough estimate: 2 words/sec
             audio = ColorClip(size=(100, 100), color=(0, 0, 0), duration=audio_duration).set_audio(None)
             audio.write_audiofile(temp_audio.name, fps=44100, logger=None)
 
-        # Create video clips from text
         clips = []
         words = narration_text.split()
-        chunk_size = 10  # Display 10 words at a time
+        chunk_size = 10
         for i in range(0, len(words), chunk_size):
             chunk = " ".join(words[i:i + chunk_size])
             clip = TextClip(chunk, fontsize=50, color='white', size=(1280, 720), bg_color='black')
             clip = clip.set_duration(audio_duration / (len(words) / chunk_size))
             clips.append(clip)
 
-        # Concatenate clips into a single video
         final_video = concatenate_videoclips(clips)
-        
-        # Add audio to video
         audio_clip = AudioFileClip(temp_audio.name)
         final_video = final_video.set_audio(audio_clip)
 
-        # Save video to temporary file
         with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_video:
             final_video.write_videofile(temp_video.name, fps=24, logger=None)
             video_path = temp_video.name
 
-        # Clean up temporary audio file
         os.unlink(temp_audio.name)
         return video_path
     except Exception as e:
@@ -152,7 +155,6 @@ def generate_text_to_video(text):
 with gr.Blocks(title="AI Tutor with Visuals") as demo:
     gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")
 
-    # Section for generating Text-based output
     with gr.Row():
         with gr.Column(scale=2):
             subject = gr.Dropdown(
@@ -179,7 +181,6 @@ with gr.Blocks(title="AI Tutor with Visuals") as demo:
             question_output = gr.Markdown(label="Comprehension Question")
             feedback_output = gr.Markdown(label="Feedback")
 
-    # Section for generating Visual output
     with gr.Row():
         with gr.Column(scale=2):
             model_selector = gr.Radio(
@@ -198,16 +199,15 @@ 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' 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.
+    1. **Text Section**: Select a subject and difficulty, type your query, and click 'Generate Lesson & Question'.
+    2. **Visual Section**: Select the model, then click 'Generate Visuals' for 3 images or 'Generate Video with Voice' for a narrated video.
     3. Review the AI-generated content to enhance your learning experience!
     """)
 
-    # Processing functions
     def process_output_text(subject, difficulty, student_input):
         try:
             tutor_output = generate_tutor_output(subject, difficulty, student_input)
-            parsed = eval(tutor_output)  # Use json.loads in production for safety
+            parsed = eval(tutor_output)  # Use json.loads in production
             return parsed["lesson"], parsed["question"], parsed["feedback"]
         except Exception as e:
             logger.error("Error parsing tutor output: %s", str(e))
@@ -229,7 +229,6 @@ with gr.Blocks(title="AI Tutor with Visuals") as demo:
             logger.error("Error in process_output_video: %s", str(e))
             return None
 
-    # Button click handlers
     submit_button_text.click(
         fn=process_output_text,
         inputs=[subject, difficulty, student_input],