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app.py

@@ -2,8 +2,8 @@ import os
 
 os.system("pip install awscli boto3 sagemaker gradio")
 
-os.system("aws configure set aws_access_key_id AKIAYIBNUHKT6GS5HZ6V")
-os.system("aws configure set aws_secret_access_key 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")
+os.system("aws configure set aws_access_key_id AKIAYIBNUHKT7UN3PKCK")
+os.system("aws configure set aws_secret_access_key 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")
 os.system("aws configure set default.region us-east-1")
 os.system("aws configure set default.output json")
 
@@ -24,7 +24,7 @@ def deploy_and_get_interface(hf_model_url):
         role = sagemaker.get_execution_role()
     except ValueError:
         iam = boto3.client('iam')
-        role = iam.get_role(RoleName='AmazonSageMaker-ExecutionRole-20231003T151671')['Role']['Arn']
+        role = iam.get_role(RoleName='Moh-work')['Role']['Arn']
 
     hf_model_id = hf_model_url.split('/')[-2]
 
@@ -52,6 +52,7 @@ def deploy_and_get_interface(hf_model_url):
     iface = gr.Interface(fn=get_prediction, 
                          inputs="text", 
                          outputs="text",
+                        
                          title="HuggingFace Model Predictor",
                          description="Enter some text and get the model's prediction!")
 
@@ -66,6 +67,7 @@ def trigger_deploy(hf_model_url):
     iface = gr.Interface(fn=get_prediction, 
                          inputs="text", 
                          outputs="text",
+                         
                          title="HuggingFace Model Predictor",
                          description="Enter some text and get the model's prediction!")
     iface.launch()