Update app.py

app.py

@@ -10,7 +10,7 @@ from huggingface_hub import InferenceClient
 
 # Project by Nymbo
 
-def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, input_image=None):
+def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
     """
     Generate images using HF's automatic provider routing
     """
@@ -18,7 +18,6 @@ def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps
     print(f"Prompt: {prompt}")
     print(f"Model: {model}")
     print(f"Custom LoRA: {custom_lora}")
-    print(f"Input Image: {input_image is not None}")
     print(f"Parameters - Steps: {steps}, CFG Scale: {cfg_scale}, Seed: {seed}, Strength: {strength}, Width: {width}, Height: {height}")
 
     # Check if the prompt is empty or None
@@ -40,6 +39,9 @@ def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps
         if not api_key:
             raise gr.Error("HF_READ_TOKEN not found. Please set your Hugging Face API token.")
         
+        # Initialize client with automatic provider selection (default is "auto")
+        client = InferenceClient(api_key=api_key)
+        
         # Determine the model to use
         if custom_lora.strip() != "":
             model_id = custom_lora.strip()
@@ -51,34 +53,16 @@ def query_with_auto_routing(prompt, model, custom_lora, is_negative=False, steps
         # Apply model-specific prompt enhancements
         enhanced_prompt = apply_model_prompt_enhancements(model, enhanced_prompt)
         
-        # Generate image using appropriate provider
-        if input_image is not None:
-            # Image-to-image generation - use Replicate provider as it supports image-to-image
-            print(f"Performing image-to-image generation with model: {model_id}")
-            print("Using Replicate provider for image-to-image generation")
-            client = InferenceClient(provider="replicate", api_key=api_key)
-            image = client.image_to_image(
-                image=input_image,
-                prompt=enhanced_prompt,
-                model=model_id,
-                width=width,
-                height=height,
-                num_inference_steps=steps,
-                guidance_scale=cfg_scale,
-                # Note: strength parameter is handled differently in image_to_image
-            )
-        else:
-            # Text-to-image generation - use automatic provider selection
-            client = InferenceClient(api_key=api_key)
-            image = client.text_to_image(
-                prompt=enhanced_prompt,
-                model=model_id,
-                width=width,
-                height=height,
-                num_inference_steps=steps,
-                guidance_scale=cfg_scale,
-                seed=seed if seed != -1 else None,
-            )
+        # Generate image using automatic provider routing
+        image = client.text_to_image(
+            prompt=enhanced_prompt,
+            model=model_id,
+            width=width,
+            height=height,
+            num_inference_steps=steps,
+            guidance_scale=cfg_scale,
+            seed=seed if seed != -1 else None,
+        )
         
         print(f'Generation {key} completed with automatic routing!')
         return image
@@ -347,11 +331,11 @@ def apply_model_prompt_enhancements(model_name, prompt):
     
     return prompt
 
-def query(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, input_image=None):
+def query(prompt, model, custom_lora, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
     """
     Main query function - now uses automatic provider routing
     """
-    return query_with_auto_routing(prompt, model, custom_lora, is_negative, steps, cfg_scale, sampler, seed, strength, width, height, input_image)
+    return query_with_auto_routing(prompt, model, custom_lora, is_negative, steps, cfg_scale, sampler, seed, strength, width, height)
 
 # Custom CSS to hide the footer in the interface
 css = """
@@ -370,24 +354,12 @@ with gr.Blocks(theme='Nymbo/Alyx_Theme') as dalle:
                 with gr.Row():
                     # Textbox for user to input the prompt
                     text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
-                with gr.Row():
-                    # Checkbox for enabling image editing
-                    enable_image_editing = gr.Checkbox(label="Enable Image Editing", value=False, elem_id="enable-image-editing")
                 with gr.Row():
                     # Textbox for custom LoRA input
                     custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
-                with gr.Row():
-                    # Image upload component (hidden by default)
-                    image_upload = gr.Image(
-                        label="Upload Image for Editing",
-                        type="pil",
-                        visible=False,
-                        elem_id="image-upload"
-                    )
                 with gr.Row():
                     # Accordion for selecting the model
-                    featured_models_accordion = gr.Accordion("Featured Models", open=False, elem_id="featured-models-accordion")
-                    with featured_models_accordion:
+                    with gr.Accordion("Featured Models", open=False):
                         # Textbox for searching models
                         model_search = gr.Textbox(label="Filter Models", placeholder="Search for a featured model...", lines=1, elem_id="model-search-input")
                         models_list = (
@@ -509,28 +481,6 @@ with gr.Blocks(theme='Nymbo/Alyx_Theme') as dalle:
                         # Update model list when search box is used
                         model_search.change(filter_models, inputs=model_search, outputs=model)
 
-                # Function to toggle image editing mode
-                def toggle_image_editing(enable_editing):
-                    if enable_editing:
-                        return (
-                            gr.update(visible=True),  # Show image upload
-                            gr.update(visible=False),  # Hide featured models accordion
-                            gr.update(value="black-forest-labs/FLUX.1-Kontext-dev")  # Set custom LoRA
-                        )
-                    else:
-                        return (
-                            gr.update(visible=False),  # Hide image upload
-                            gr.update(visible=True),  # Show featured models accordion
-                            gr.update(value="")  # Clear custom LoRA
-                        )
-
-                # Set up the toggle functionality
-                enable_image_editing.change(
-                    toggle_image_editing,
-                    inputs=[enable_image_editing],
-                    outputs=[image_upload, featured_models_accordion, custom_lora]
-                )
-
     # Tab for advanced settings
     with gr.Tab("Advanced Settings"):
         with gr.Row():
@@ -618,9 +568,6 @@ with gr.Blocks(theme='Nymbo/Alyx_Theme') as dalle:
         with gr.Accordion("Advanced Settings Overview", open=False):
             gr.Markdown(
             """
-            ## Enable Image Editing
-            ###### When enabled, this feature allows you to upload an image and use the FLUX.1-Kontext-dev model to edit it. This model specializes in image-to-image transformations, allowing you to modify existing images based on your text prompts. The system automatically uses the Replicate provider for image-to-image generation as it supports this functionality.
-
             ## Negative Prompt
             ###### This box is for telling the AI what you don't want in your images. Think of it as a way to avoid certain elements. For instance, if you don't want blurry images or extra limbs showing up, this is where you'd mention it.
 
@@ -654,7 +601,7 @@ with gr.Blocks(theme='Nymbo/Alyx_Theme') as dalle:
         image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
 
     # Set up button click event to call the main query function
-    text_button.click(query, inputs=[text_prompt, model, custom_lora, negative_prompt, steps, cfg, method, seed, strength, width, height, image_upload], outputs=image_output)
+    text_button.click(query, inputs=[text_prompt, model, custom_lora, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
 
 print("Launching Gradio interface...")  # Debug log
 # Launch the Gradio interface without showing the API or sharing externally