Update app.py

app.py

@@ -1,107 +1,91 @@
 import gradio as gr
 import requests
+import json
+import PIL.Image
+from io import BytesIO
 
 def generate_image(prompt):
-  apiUrl = 'https://apiv2.makeai.run/v2/txt2img' # Replace with your API URL
-  data = {
-    "mode": "url",
-    "model": "AOM3A1B_orangemixs.safetensors",
-    "tiling": false,
-    "batch_size": 1,
-    "prompt": prompt,
-    "negative_prompt": "worst quality, lowres",
-    "seed": 1234,
-    "scheduler": "Euler a",
-    "n_iter": 1,
-    "steps": 30,
-    "cfg": 11.0,
-    "offset_noise": 0.0,
-    "width": 1024,
-    "height": 1024,
-    "clip_skip": 1,
-    "loras": [
-      {
-        "name": "",
-        "strength": 1.0
-      }
-    ],
-    "embeddings": [
-      {
-        "name": "",
-        "strength": 1.0
-      }
-    ],
-    "vae": "vae-ft-mse-840000-ema-pruned.ckpt",
-    "restore_faces": false,
-    "fr_model": "CodeFormer",
-    "codeformer_weight": 0.5,
-    "enable_hr": false,
-    "denoising_strength": 0.75,
-    "hr_scale": 2,
-    "hr_upscale": "None",
-    "img2img_ref_img_type": "piece",
-    "img2img_resize_mode": 0,
-    "img2img_denoising_strength": 0.75,
-    "controlnet_enabled": false,
-    "controlnet_ref_img_type": "piece",
-    "controlnet_guessmode": false,
-    "controlnet_module": "canny",
-    "controlnet_model": "control_v11p_sd15_softedge",
-    "controlnet_weight": 1,
-    "controlnet_guidance_start": 0,
-    "controlnet_guidance_end": 1,
-    "controlnet_ref_img_url": "",
-    "controlnet_mask": [],
-    "controlnet_resize_mode": "Scale to Fit (Inner Fit)",
-    "controlnet_lowvram": false,
-    "controlnet_processor_res": 512,
-    "controlnet_threshold_a": 100,
-    "controlnet_threshold_b": 200
-  }
-  headers = {
-    'Content-Type': 'application/json',
-    'token': '514f7ecde6a5434dbab5c6579311ad82' # Replace with your API token
-  }
-  response = requests.post(apiUrl, json=data, headers=headers)
-  responseData = response.json()
-  if responseData and responseData['results'] and responseData['results'][0]:
-    return responseData['results'][0]
-  else:
-    return "No image generated"
+    # Define the API endpoint
+    apiUrl = "https://apiv2.makeai.run/v2/txt2img"
 
-# Uncomment the following lines if you want to use the control image component
-# def generate_image(prompt, control_image):
-#   apiUrl = 'https://apiv2.makeai.run/v2/txt2img' # Replace with your API URL
-#   data = {
-#     ...
-#     "controlnet_ref_img_url": control_image,
-#     ...
-#   }
-#   headers = {
-#     ...
-#   }
-#   response = requests.post(apiUrl, json=data, headers=headers)
-#   responseData = response.json()
-#   if responseData and responseData['results'] and responseData['results'][0]:
-#     return responseData['results'][0]
-#   else:
-#     return "No image generated"
+    # Define the request headers
+    headers = {
+        "Content-Type": "application/json",
+        "token": "514f7ecde6a5434dbab5c6579311ad82"
+    }
 
-title = gr.outputs.Textbox(label="Freedom Demonstration")
-prompt = gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here", label="Prompt")
-image = gr.outputs.Image(label="Generated Image")
+    # Define the request body
+    body = {
+        "mode": "url",
+        "model": "AOM3A1B_orangemixs.safetensors",
+        "tiling": False,
+        "batch_size": 1,
+        "prompt": prompt,
+        "negative_prompt": "worst quality, lowres",
+        "seed": 1234,
+        "scheduler": "Euler a",
+        "n_iter": 1,
+        "steps": 25,
+        "cfg": 11.0,
+        "offset_noise": 0.0,
+        "width": 512,
+        "height": 512,
+        "clip_skip": 1,
+        "loras": [{"name": "", "strength": 1.0}],
+        "embeddings": [{"name": "", "strength": 1.0}],
+        "vae": "vae-ft-mse-840000-ema-pruned.ckpt",
+        "restore_faces": False,
+        "fr_model": "CodeFormer",
+        "codeformer_weight": 0.5,
+        "enable_hr": False,
+        "denoising_strength": 0.75,
+        "hr_scale": 2,
+        "hr_upscale": "None",
+        "img2img_ref_img_type": "piece",
+        "img2img_resize_mode": 0,
+        "img2img_denoising_strength": 0.75,
+        "controlnet_enabled": False,
+        "controlnet_ref_img_type": "piece",
+        "controlnet_guessmode": False,
+        "controlnet_module": "canny",
+        "controlnet_model": "control_v11p_sd15_softedge",
+        "controlnet_weight": 1,
+        "controlnet_guidance_start": 0,
+        "controlnet_guidance_end": 1,
+        "controlnet_ref_img_url": "https://upload.wikimedia.org/wikipedia/commons/d/d1/Image_not_available.png",
+        "controlnet_mask": [],
+        "controlnet_resize_mode": "Scale to Fit (Inner Fit)",
+        "controlnet_lowvram": False,
+        "controlnet_processor_res": 512,
+        "controlnet_threshold_a": 100,
+        "controlnet_threshold_b": 200
+    }
 
-# Uncomment the following line if you want to use the control image component
-# control_image = gr.inputs.Image(label="Control Image (optional)")
+    # Send the request
+    response = requests.post(apiUrl, headers=headers, data=json.dumps(body))
 
+    # Check the response status
+    if response.status_code == 200:
+        # Get the image URL from the response
+        image_url = json.loads(response.text)['results'][0]
+
+        # Get the image from the URL
+        image_response = requests.get(image_url)
+        image = PIL.Image.open(BytesIO(image_response.content))
+
+        return image
+    else:
+        raise Exception("API request failed with status code " + str(response.status_code))
+
+# Define the Gradio interface
 iface = gr.Interface(
-  fn=generate_image,
-  inputs=[prompt], # Add control_image to the list if you want to use it
-  outputs=[title, image],
-  title="Freedom Demonstration",
-  description="This is a Gradio app that uses an API to generate images based on text prompts.",
-  live=False,
-  layout="vertical"
+    fn=generate_image, 
+    inputs="text", 
+    outputs="image",
+    title="Freedom Demonstration",
+    description="Enter a prompt and generate an image.",
+    allow_flagging=False
 )
 
+# Launch the app
 iface.launch()