Update app.py

app.py

@@ -2,13 +2,11 @@ import torch
 import os
 import requests
 import logging
-import gc
 from pathlib import Path
-
 from transformers import CLIPModel, CLIPProcessor, AutoTokenizer, MarianMTModel, MarianTokenizer
 from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
 import gradio as gr
-from typing import List, Tuple, Optional, Dict, Any
+from typing import List, Tuple, Optional, Any
 from dataclasses import dataclass
 
 # Configure logging
@@ -25,7 +23,7 @@ def download_model(model_url: str, model_path: str):
             logger.info(f"Downloading model from {model_url}...")
             response = requests.get(model_url, stream=True)
             response.raise_for_status()
-            
+
             total_size = int(response.headers.get('content-length', 0))
             block_size = 1024 * 1024  # 1 MB chunks
             downloaded_size = 0
@@ -78,7 +76,6 @@ class EnhancedBanglaSDGenerator:
     def _load_banglaclip_model(self):
         """Load BanglaCLIP model from Hugging Face directly"""
         try:
-            # Use the Hugging Face model directly instead of local weight file
             model = CLIPModel.from_pretrained("Mansuba/BanglaCLIP13")
             return model.to(self.device)
         except Exception as e:
@@ -113,7 +110,9 @@ class EnhancedBanglaSDGenerator:
             logger.error(f"Error initializing models: {str(e)}")
             raise RuntimeError(f"Failed to initialize models: {str(e)}")
 
-    # [Rest of the existing implementation remains the same]
+    def _initialize_stable_diffusion(self):
+        """Load and initialize Stable Diffusion pipeline"""
+        pass  # Your existing code for initializing Stable Diffusion
 
 def create_gradio_interface():
     """Create and configure the Gradio interface."""
@@ -156,8 +155,20 @@ def create_gradio_interface():
             cleanup_generator()
             return None, f"ছবি তৈরি ব্যর্থ হয়েছে: {str(e)}"
 
-    # [Gradio interface configuration remains the same]
+    with gr.Blocks() as demo:
+        text_input = gr.Textbox(label="Text", placeholder="Enter your prompt here...")
+        num_images_input = gr.Slider(minimum=1, maximum=5, value=1, label="Number of Images")
+        steps_input = gr.Slider(minimum=1, maximum=100, value=50, label="Steps")
+        guidance_scale_input = gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale")
+        seed_input = gr.Number(label="Seed", optional=True)
+
+        output_images = gr.Gallery(label="Generated Images")
+
+        generate_button = gr.Button("Generate Images")
+        generate_button.click(generate_images, inputs=[text_input, num_images_input, steps_input, guidance_scale_input, seed_input], outputs=[output_images])
+
+    return demo
 
 if __name__ == "__main__":
     demo = create_gradio_interface()
-    demo.queue().launch(share=True, debug=True)
+    demo.queue().launch(share=True, debug=True)