Update app.py

app.py

@@ -1,11 +1,18 @@
 import streamlit as st
-from clarifai.client.model import Model
-import base64
+from clarifai.rest import ClarifaiApp
 from PIL import Image
 from io import BytesIO
+import base64
 
+# Your Personal Access Token
 PAT = '96eccae9a5944c1097477f0529306410'
 
+# Initialize Clarifai app
+app = ClarifaiApp(api_key=PAT)
+
+# Specify the model ID (replace 'YOUR_MODEL_ID' with the actual model ID)
+model = app.models.get('YOUR_MODEL_ID')
+
 # Streamlit page configuration
 st.title('Image Generation with Clarifai')
 st.write('Enter a prompt and generate an image.')
@@ -15,11 +22,11 @@ user_input = st.text_input('Enter your image prompt')
 
 # Process the input when a prompt is given
 if user_input:
-    # Your existing code with slight modifications
-    inference_params = dict(guidance_scale = "medium")
-    model_prediction = Model("https://clarifai.com/stability-ai/stable-diffusion-2/models/stable-diffusion-xl").predict_by_bytes(user_input.encode(), input_type="text", inference_params=inference_params)
+    # Get model prediction
+    model_prediction = model.predict_by_bytes(user_input.encode(), input_type="text")
     
-    output_base64 = model_prediction.outputs[0].data.image.base64
+    # Extract the image data
+    output_base64 = model_prediction['outputs'][0]['data']['image']['base64']
 
     # Convert base64 to PIL Image
     image_data = base64.b64decode(output_base64)
@@ -29,4 +36,4 @@ if user_input:
     st.image(image, caption='Generated Image', use_column_width=True)
 
     # Optional: Print additional image info
-    st.write(model_prediction.outputs[0].data.image.image_info)
+    st.write(model_prediction['outputs'][0]['data']['image']['image_info'])