Update app.py

app.py

@@ -38,59 +38,59 @@ def generate_files(title="Text Generation Tool", emoji="🌖", colorFrom="blue",
 
     # Generate app.py content
     app_py_content = '''from transformers.tools.base import launch_gradio_demo
-from {} import {}Tool
-launch_gradio_demo({}Tool)
-'''.format(tool_name, tool_name.capitalize(), tool_name.capitalize())
+        from {} import {}Tool
+        launch_gradio_demo({}Tool)
+        '''.format(tool_name, tool_name.capitalize(), tool_name.capitalize())
 
     # Generate requirements.txt content
     requirements_content = '''transformers>=4.29.0
-# diffusers
-accelerate
-torch
-'''
+        # diffusers
+        accelerate
+        torch
+        '''
 
     # Generate text_generator.py content
     text_generator_py_content = '''import os
-from transformers import pipeline
-class {}Tool(Tool):
-    name = "{}"
-    description = (
-        "{}"
-    )
-    inputs = ["text"]
-    outputs = ["text"]
-    def __call__(self, prompt: str):
-        token = os.environ['hf']
-        text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)
-        generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
-        print(generated_text)
-        return generated_text
-'''.format(tool_name.capitalize(), tool_name, tool_description)
+    from transformers import pipeline
+    class {}Tool(Tool):
+        name = "{}"
+        description = (
+            "{}"
+        )
+        inputs = ["text"]
+        outputs = ["text"]
+        def __call__(self, prompt: str):
+            token = os.environ['hf']
+            text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)
+            generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
+            print(generated_text)
+            return generated_text
+    '''.format(tool_name.capitalize(), tool_name, tool_description)
 
     # Write content to files
-    with open("README.md", "w") as readme_file:
+    with open("new/README.md", "w") as readme_file:
         readme_file.write(readme_content)
 
-    with open("tool_config.json", "w") as tool_config_file:
+    with open("new/tool_config.json", "w") as tool_config_file:
         tool_config_file.write(tool_config_json)
 
-    with open("app.py", "w") as app_py_file:
+    with open("new/app.py", "w") as app_py_file:
         app_py_file.write(app_py_content)
 
-    with open("requirements.txt", "w") as requirements_file:
+    with open("new/requirements.txt", "w") as requirements_file:
         requirements_file.write(requirements_content)
 
-    with open("{}.py".format(tool_name), "w") as text_generator_py_file:
+    with open("new/{}.py".format(tool_name), "w") as text_generator_py_file:
         text_generator_py_file.write(text_generator_py_content)
 
     # Upload the files to the Hugging Face Hub
-#    hub = TransformersHub(organization="your_organization")
-#    hub.create(
-#        model_id=tool_name,
-#        repo_path="./",
-#        commit_message="Upload {} tool".format(tool_name),
-#        private=False
-#    )
+    hub = TransformersHub(organization="your_organization")
+    hub.create(
+        model_id=tool_name,
+        repo_path="./new/",
+        commit_message="Upload {} tool".format(tool_name),
+        private=False
+    )
 
     # Return the generated files for download
     return "README.md", "tool_config.json", "app.py", "requirements.txt", "{}.py".format(tool_name)