Update app.py

app.py

@@ -1,10 +1,37 @@
-from diffusers import AutoPipelineForText2Image
-import torch
+import gradio as gr
+import requests
+import json
 
-pipeline = AutoPipelineForText2Image.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
-prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"
+url = "https://nexra.aryahcr.cc/api/image/complements"
+headers = {
+    "Content-Type": "application/json"
+}
 
-image = pipeline(prompt, num_inference_steps=25).images[0]
-image
+def generate_image(prompt, model):
+    data = {
+        "prompt": prompt,
+        "model": model,
+    }
+    try:
+        response = requests.post(url, headers=headers, data=json.dumps(data))
+        raw_response = response.text.strip()
+
+        if response.status_code == 200:
+            cleaned_response = raw_response.lstrip('_')
+            response_data = json.loads(cleaned_response)
+            
+            if 'images' in response_data and isinstance(response_data['images'], list):
+                image_url = response_data['images'][0]
+                return image_url
+            else:
+                return "Error: No image data in the response."
+        else:
+            return f"Error: {response.status_code}"
+    except json.JSONDecodeError:
+        return "Error: Invalid JSON response."
+    except Exception as e:
+        return str(e)
+
+
+iface = gr.Interface(fn=generate_image, inputs=[gr.Textbox(label="Enter prompt"), gr.Radio(["dalle2"], label="Select Model")], outputs="image", title="DALLE2 Generation", description="Disclaimer: This uses the Nexra API for image generation. I cannot guarantee rate limits, if you do not recieve an image please try again. I will change to use a different API soon. DALL-E 3 is not supported yet.")
+iface.launch()