Update app.py

app.py

@@ -1,30 +1,40 @@
-
-import gradio as gr
 import requests
+import gradio as gr
 from PIL import Image
-import io
 import numpy as np
 
-api_key = "sk-CED85fi0ZhUDMWg4GvFQ5k53o7yoL7WOaPyPQcb8zPi7eDGi"
+# Replace with your Dream API key
+api_key = "YOUR_API_KEY_HERE"
+
+def generate_image(prompt):
+    # Set up the request headers
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}"
+    }
 
-def dream_studio(user_input):
-    # Create the Dream Studio image
-    response = requests.post(
-        'https://beta.dreamstudio.ai/api/creations/images/generate',
-        headers={'Authorization': f'Bearer {api_key}'},
-        json={
-            'model': 'biggan-deep-256',
-            'text': user_input
-        }
-    )
+    # Set up the request data
+    data = {
+        "model": "stable-diffusion",
+        "prompt": prompt,
+        "steps": 100,
+        "batch_size": 1,
+        "gamma": 0.99,
+        "device": "cpu"
+    }
+
+    # Send the request to Dream's API
+    response = requests.post("https://api.dream.co/stable-diffusion/generate", json=data, headers=headers)
     response.raise_for_status()
-    
-    # Convert the image to a format that Gradio can display
-    img_bytes = io.BytesIO(response.content)
-    img = Image.open(img_bytes)
-    img_arr = np.array(img)
-    
-    return img_arr
 
-iface = gr.Interface(fn=dream_studio, inputs="text", outputs="image", title="bhAI (text to image)")
+    # Extract the image URL from the response
+    image_url = response.json()["data"][0]["url"]
+
+    # Download and display the image using PIL and Gradio
+    image_bytes = requests.get(image_url).content
+    image = Image.open(io.BytesIO(image_bytes))
+    image_arr = np.array(image)
+    return image_arr
+
+iface = gr.Interface(fn=generate_image, inputs="text", outputs="image", title="bhAI (text to image using Dream Studio's Stable Difusion)", description="Enter a prompt to generate an image.")
 iface.launch()