Update app.py

adding blip and negative prompt

app.py

@@ -8,19 +8,23 @@ import numpy as np
 import cv2
 
 from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
-from utils.planner import extract_scene_plan, generate_prompt_variations_from_scene  # 🧠 Brain Layer
+from utils.planner import (
+    extract_scene_plan,
+    generate_prompt_variations_from_scene,
+    generate_negative_prompt_from_scene  # ✅ Added negative prompt generator
+)
 
 # ----------------------------
 # 🔧 Device Setup
 # ----------------------------
-device = "cpu"  # ✅ Using CPU for now
+device = "cpu"
 dtype = torch.float32
 
 # ----------------------------
-# ✅ Load ControlNet + SDXL Model (Corrected)
+# ✅ Load ControlNet + SDXL Model
 # ----------------------------
 controlnet = ControlNetModel.from_pretrained(
-    "diffusers/controlnet-canny-sdxl-1.0",  # Use official Canny + SDXL ControlNet model
+    "diffusers/controlnet-canny-sdxl-1.0",
     torch_dtype=dtype
 )
 
@@ -54,30 +58,35 @@ def generate_canny_map(image: Image.Image) -> Image.Image:
 def process_image(prompt, image, num_variations):
     try:
         print("🧠 Prompt received:", prompt)
-
         if image is None:
             raise ValueError("🚫 Uploaded image is missing or invalid.")
 
-        # Step 1: Extract scene plan
-        scene_plan = extract_scene_plan(prompt)
+        # Step 1: Scene Planning
+        scene_plan = extract_scene_plan(prompt, image)
         print("🧠 Scene plan extracted:", scene_plan)
 
-        # Step 2: Generate enriched prompt variations
+        # Step 2: Prompt Variations
         prompt_list = generate_prompt_variations_from_scene(scene_plan, prompt, num_variations)
         print("🧠 Enriched Prompts:")
         for i, p in enumerate(prompt_list):
             print(f"  {i+1}: {p}")
 
-        # Step 3: Prepare image and Canny edge
+        # Step 3: Negative Prompt (auto-generated)
+        negative_prompt = generate_negative_prompt_from_scene(scene_plan)
+        print("🚫 Negative Prompt:", negative_prompt)
+
+        # Step 4: Prepare image & canny
         image = image.resize((1024, 1024)).convert("RGB")
         canny_map = generate_canny_map(image)
 
+        # Step 5: Generate images
         outputs = []
         for i, enriched_prompt in enumerate(prompt_list):
             print(f"🎨 Generating image {i+1}...")
             try:
                 result = pipe(
                     prompt=enriched_prompt,
+                    negative_prompt=negative_prompt,
                     image=image,
                     control_image=canny_map,
                     num_inference_steps=30,
@@ -99,7 +108,7 @@ def process_image(prompt, image, num_variations):
 # 🖼 Gradio UI
 # ----------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 NewCrux AI — SDXL + Canny (CPU Mode)\nUpload a product image, enter a prompt, and generate enhanced visuals using ControlNet.")
+    gr.Markdown("## 🧠 NewCrux AI — SDXL + ControlNet Canny (CPU Mode)\nUpload a product image, enter a prompt, and generate enhanced visuals with GPT + BLIP + SDXL.")
 
     with gr.Row():
         with gr.Column():
@@ -120,4 +129,3 @@ with gr.Blocks() as demo:
     )
 
 demo.launch()
-