Update app.py

app.py

@@ -1,61 +1,43 @@
 import gradio as gr
-import requests
 import os
+from huggingface_hub import InferenceClient
 from PIL import Image
-from io import BytesIO
-from tqdm import tqdm
-import time
 
-# Load token from .env if available
-token = os.environ["HF_TOKEN"]  # Place your token in a .env file or set it in the environment
+# Load token from environment
+token = os.environ["HF_TOKEN"]
 
-# Define repository and trigger
-repo = "artificialguybr/TshirtDesignRedmond-V2"
+# Setup inference client
+client = InferenceClient(
+    model="artificialguybr/TshirtDesignRedmond-V2",
+    provider="fal-ai",
+    token=token,
+)
+
+# Trigger words as per model instructions
 trigger_word = "T shirt design, TshirtDesignAF, "
 
 def generate_image(prompt):
-    print("Generating image with prompt:", prompt)
-    api_url = f"https://api-inference.huggingface.co/models/{repo}"
-    
-    headers = {
-        "Authorization": f"Bearer {token}"
-    }
-
     full_prompt = f"{prompt} {trigger_word}"
-    payload = {
-        "inputs": full_prompt,
-        "parameters": {
-            "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
+    print("Generating image with prompt:", full_prompt)
+    
+    # Generate image
+    image = client.text_to_image(
+        full_prompt,
+        params={
+            "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details...)",
             "num_inference_steps": 30,
-            "scheduler": "DPMSolverMultistepScheduler"
-        },
-    }
-
-    error_count = 0
-    pbar = tqdm(total=None, desc="Loading model")
-    while True:
-        print("Sending request to API...")
-        response = requests.post(api_url, headers=headers, json=payload)
-        print("API response status code:", response.status_code)
-        if response.status_code == 200:
-            print("Image generation successful!")
-            return Image.open(BytesIO(response.content))
-        elif response.status_code == 503:
-            time.sleep(1)
-            pbar.update(1)
-        elif response.status_code == 500 and error_count < 5:
-            time.sleep(1)
-            error_count += 1
-        else:
-            print("API Error:", response.status_code)
-            raise Exception(f"API Error: {response.status_code}")
+            "scheduler": "DPMSolverMultistepScheduler",
+        }
+    )
+    return image
 
+# Gradio interface
 iface = gr.Interface(
     fn=generate_image,
     inputs=gr.Textbox(lines=2, placeholder="Type your prompt here..."),
     outputs="image",
     title="TShirt Design XL Image Generator",
-    description="Powered by Redmond.AI — This fine-tuned SDXL model generates T-shirt designs from prompts. Trigger tags: 'T shirt design, TshirtDesignAF'.",
+    description="Powered by Redmond.AI — Generates T-shirt designs from prompts. Trigger tags: 'T shirt design, TshirtDesignAF'.",
     examples=[["Cute Panda"], ["Skull"]]
 )