🚀 Updated app with Brain Layer + HuggingFace model improvements

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

app.py

@@ -9,52 +9,68 @@ from io import BytesIO
 
 from utils.planner import extract_scene_plan  # 🧠 Brain Layer
 
-# 🔐 Hugging Face + OpenAI keys (use Secrets)
+# 🔐 Hugging Face keys
 HF_API_KEY = os.getenv("HF_API_KEY")
-SDXL_MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
+SDXL_MODEL_ID = "fal-ai/fast-sdxl/image-to-image"  # ✅ Correct model for image-to-image
 SDXL_API_URL = f"https://api-inference.huggingface.co/models/{SDXL_MODEL_ID}"
 HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"}
 
-# 🚀 Image generation (no ControlNet)
+# 🚀 Image generation (img2img)
 def process_image(prompt, image, num_variations):
-    # Step 1: Extract planning JSON from Brain Layer
-    reasoning_json = extract_scene_plan(prompt)
-
-    # Step 2: Encode image once
-    buffered = BytesIO()
-    image.save(buffered, format="JPEG")
-    img_bytes = buffered.getvalue()
-    encoded_image = base64.b64encode(img_bytes).decode("utf-8")
-
-    # Step 3: Generate multiple variations using base SDXL
-    outputs = []
-
-    for i in range(num_variations):
-        payload = {
-            "inputs": {
-                "prompt": prompt,
+    try:
+        print("🧠 Prompt received:", prompt)
+        
+        # Step 1: Brain Layer
+        reasoning_json = extract_scene_plan(prompt)
+        print("🧠 Scene plan extracted:", reasoning_json)
+
+        # Step 2: Encode input image
+        buffered = BytesIO()
+        image.save(buffered, format="JPEG")
+        img_bytes = buffered.getvalue()
+        encoded_image = base64.b64encode(img_bytes).decode("utf-8")
+
+        # Step 3: Send image + prompt to HF API
+        outputs = []
+        for i in range(num_variations):
+            payload = {
                 "image": encoded_image,
-                "negative_prompt": "blurry, deformed, cropped"
-            },
-            "options": {"wait_for_model": True}
-        }
+                "prompt": prompt,
+                "negative_prompt": "blurry, deformed, cropped",
+                "num_inference_steps": 25,
+                "guidance_scale": 7.5
+            }
 
-        try:
+            print(f"📤 Sending request to HF (variation {i+1})")
             response = requests.post(SDXL_API_URL, headers=HEADERS, json=payload)
+
             if response.status_code == 200:
-                result_image = Image.open(BytesIO(response.content))
-                outputs.append(result_image)
+                try:
+                    result_json = response.json()
+                    if "images" in result_json:
+                        base64_img = result_json["images"][0]
+                        result_image = Image.open(BytesIO(base64.b64decode(base64_img)))
+                        outputs.append(result_image)
+                        print(f"✅ Decoded image variation {i+1} successfully")
+                    else:
+                        print(f"⚠️ No 'images' key found in response")
+                        outputs.append("❌ No image in response.")
+                except Exception as decode_err:
+                    print("❌ Image decode error:", decode_err)
+                    outputs.append("❌ Failed to decode image.")
             else:
+                print(f"❌ HF API error: {response.status_code} - {response.text}")
                 outputs.append(f"Error {response.status_code}: {response.text}")
-        except Exception as e:
-            outputs.append(f"Exception: {e}")
 
-    return outputs, reasoning_json
+        return outputs, reasoning_json
 
+    except Exception as e:
+        print("❌ General Exception in process_image:", e)
+        return ["Processing error occurred"], {"error": str(e)}
 
 # 🎨 Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 NewCrux AI Demo: Image-to-Image using Base SDXL + Brain Layer")
+    gr.Markdown("# 🧠 NewCrux AI Demo: Image-to-Image using Fast SDXL + Brain Layer")
 
     with gr.Row():
         with gr.Column():
@@ -78,5 +94,5 @@ with gr.Blocks() as demo:
         outputs=[output_gallery, json_output]
     )
 
-demo.launch()
+demo.launch(share=True)
 

requirements.txt

@@ -2,4 +2,4 @@ gradio
 requests
 Pillow
 openai>=1.0.0,<2.0.0
-
+python-dotenv

trial.py

@@ -0,0 +1,17 @@
+# test_openai.py
+
+import os
+from openai import OpenAI
+
+from dotenv import load_dotenv
+load_dotenv()
+# Set your API key directly or use an environment variable
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# Simple test: ask ChatGPT something basic
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    messages=[{"role": "user", "content": "Say hello"}],
+)
+
+print(response.choices[0].message.content)

utils/planner.py

@@ -1,9 +1,11 @@
 # utils/planner.py
 
+import openai
 import os
-import json
-import logging
 from openai import OpenAI
+from dotenv import load_dotenv
+import json
+load_dotenv()
 
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
@@ -21,7 +23,7 @@ Respond ONLY in raw JSON format. Do NOT include explanations.
 def extract_scene_plan(prompt: str) -> dict:
     try:
         response = client.chat.completions.create(
-            model="gpt-4-0125-preview",
+            model="gpt-4o-mini-2024-07-18",
             messages=[
                 {"role": "system", "content": SYSTEM_INSTRUCTIONS},
                 {"role": "user", "content": prompt}
@@ -29,16 +31,12 @@ def extract_scene_plan(prompt: str) -> dict:
             temperature=0.3,
             max_tokens=500
         )
-
-        content = response.choices[0].message.content.strip()
-
-        # Remove common formatting issues
-        content = content.strip("`").replace("json", "")
-
-        return json.loads(content)
+        json_output = response.choices[0].message.content
+        print("🧠 Scene Plan (Raw):", json_output)
+        return json.loads(json_output) # Be cautious: Use `json.loads()` if possible
 
     except Exception as e:
-        logging.exception("LLM Error:")
+        print("❌ extract_scene_plan() Error:", e)
         return {
             "scene": None,
             "subject": None,