Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+import requests
+import os
+from PIL import Image
+from io import BytesIO
+from tqdm import tqdm
+import time
+
+# Defining the repository information and the trigger word
+repo = "artificialguybr/TshirtDesignRedmond-V2"
+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}"
+    #token = os.getenv("API_TOKEN")  # Uncomment and use your Hugging Face API token
+    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)",
+            "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))  # Changed to match the first code
+        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}")
+
+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="Make designs for your clothes",
+    examples=[["Cute Panda"], ["Skull"]]
+)
+
+print("Launching Gradio interface...")
+iface.launch()