update system prompt and interface labels for clarity and consistency

app.py

@@ -31,11 +31,11 @@ PRELOAD_MODELS = False
 # Default system prompt for text generation
 DEFAULT_SYSTEM_PROMPT = """You are a product designer with strong knowledge in text-to-image generation. You will receive a product request in the form of a brief description, and your mission will be to imagine a new product design that meets this need.
 
-The deliverable (generated response) will be exclusively a text prompt for the FLUX.1-schnell text-to-image AI.
+The deliverable (generated response) will be exclusively a text prompt for the FLUX.1-dev text-to-image AI.
 
 This prompt should include a visual description of the object explicitly mentioning the essential aspects of its function.
 Additionally, you should explicitly mention in this prompt the aesthetic/photo characteristics of the image rendering (e.g., photorealistic, high quality, focal length, grain, etc.), knowing that the image will be the main image of this object in the product catalog. The background of the generated image must be entirely white.
-The prompt should be without narration, can be long but must not exceed 77 tokens."""
+The prompt should be without narration."""
 
 # Default Flux parameters
 DEFAULT_SEED = 42
@@ -402,7 +402,7 @@ def create_interface():
         output_state = gr.State(None)
 
         with gr.Column(elem_id="col-container"):
-            gr.Markdown("# Text to Product\nUsing Mistral-7B + FLUX.1-dev + Trellis")
+            gr.Markdown("# Text to 3D\nFocusing on product creation\nUsing Mistral-7B + FLUX.1-dev + Trellis")
             
             prompt = gr.Text(
                 show_label=False,
@@ -486,10 +486,15 @@ def create_interface():
 
             gr.Examples(
                 examples=examples,
-                fn=process_example_pipeline,
-                inputs=[prompt],
+                # fn=process_example_pipeline,
+                fn=refine_prompt,
+                inputs=[prompt, system_prompt],
                 outputs=[refined_prompt, message_box],
                 cache_examples=True,
+            ).then(
+                fn=generate_image,
+                inputs=[refined_prompt, flux_seed, flux_randomize_seed, width, height, num_inference_steps],
+                outputs=[generated_image, message_box]
             )
 
         gr.on(