image generation delete

huggingface_inference_node.py

@@ -5,7 +5,6 @@ from datetime import datetime
 import anthropic
 from groq import Groq
 from openai import OpenAI
-from gradio_client import Client
 
 huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
 groq_api_key = os.getenv("GROQ_API_KEY")
@@ -23,8 +22,6 @@ class LLMInferenceNode:
             api_key=sambanova_api_key,
             base_url="https://api.sambanova.ai/v1",
         )
-        self.huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-        self.flux_client = Client("KingNish/Realtime-FLUX", hf_token=self.huggingface_token)
 
     def generate(
         self,
@@ -180,18 +177,3 @@ You are allowed to make up film and branding names, and do them like 80's, 90's
         except Exception as e:
             print(f"An error occurred: {e}")
             return f"Error occurred while processing the request: {str(e)}"
-
-    def generate_image(self, prompt, seed=42, width=1024, height=1024):
-        try:
-            result = self.flux_client.predict(
-                prompt=prompt,
-                seed=seed,
-                width=width,
-                height=height,
-                api_name="/generate_image"
-            )
-            # Extract the image path from the result tuple
-            image_path = result[0]
-            return image_path
-        except Exception as e:
-            raise Exception(f"Error generating image: {str(e)}")

ui_components.py

@@ -141,14 +141,6 @@ def create_interface():
                 generate_text_button = gr.Button("Generate Prompt with LLM")
                 text_output = gr.Textbox(label="Generated Text", lines=10, show_copy_button=True)
 
-            with gr.Column(scale=2):
-                with gr.Accordion("Image Generation", open=True):
-                    image_output = gr.Image(label="Generated Image", type="filepath")
-                    generate_image_button = gr.Button("Generate Image")
-                    image_seed = gr.Number(label="Image Seed", value=42, step=1)
-                    image_width = gr.Slider(label="Width", minimum=512, maximum=2048, value=1024, step=64)
-                    image_height = gr.Slider(label="Height", minimum=512, maximum=2048, value=1024, step=64)
-
         def create_caption(image, model):
             if image is not None:
                 if model == "Florence-2":
@@ -272,21 +264,4 @@ def create_interface():
             ]
         )
 
-        # Function to generate image
-        def generate_image(text, seed, width, height):
-            try:
-                image_path = llm_node.generate_image(text, seed=seed, width=width, height=height)
-                print(f"Image generated: {image_path}")
-                return image_path
-            except Exception as e:
-                print(f"An error occurred while generating the image: {e}")
-                return None
-
-        # Connect the image generation button
-        generate_image_button.click(
-            generate_image,
-            inputs=[text_output, image_seed, image_width, image_height],
-            outputs=[image_output]
-        )
-
     return demo