prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  What should `cog predict` do if you don't pass an input name?
  The syntax of `cog predict` is this:
  
      cog predict -i image=@image.jpg
  
  But you can also do this:
  
      cog predict -i @image.jpg
  
  Which implicitly means an input name of `input`. This is a neat short hand but a bit weird for a few reasons:
  
  - `input` is a Python built-in, so we should really be encouraging using that as a variable name.
  - It is a magic name.
  - For a sufficiently complex model, you probably don't want to call it `input`.
  
  What could we do that is better here? Maybe if you don't pass a name, it defaults to the _first_ input defined, rather than a magic name? This is vaguely backwards compatible, which is neat.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `pkg/cli/init-templates/predict.py`
  Content:
  ```
  1 # Prediction interface for Cog ⚙️
  2 # https://github.com/replicate/cog/blob/main/docs/python.md
  3 
  4 from cog import BasePredictor, Input, Path
  5 
  6 
  7 class Predictor(BasePredictor):
  8     def setup(self):
  9         """Load the model into memory to make running multiple predictions efficient"""
  10         # self.model = torch.load("./weights.pth")
  11 
  12     def predict(
  13         self,
  14         input: Path = Input(description="Grayscale input image"),
  15         scale: float = Input(
  16             description="Factor to scale image by", ge=0, le=10, default=1.5
  17         ),
  18     ) -> Path:
  19         """Run a single prediction on the model"""
  20         # processed_input = preprocess(input)
  21         # output = self.model(processed_input, scale)
  22         # return postprocess(output)
  23
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/pkg/cli/init-templates/predict.py b/pkg/cli/init-templates/predict.py
  --- a/pkg/cli/init-templates/predict.py
  +++ b/pkg/cli/init-templates/predict.py
  @@ -11,12 +11,12 @@
   
       def predict(
           self,
  -        input: Path = Input(description="Grayscale input image"),
  +        image: Path = Input(description="Grayscale input image"),
           scale: float = Input(
               description="Factor to scale image by", ge=0, le=10, default=1.5
           ),
       ) -> Path:
           """Run a single prediction on the model"""
  -        # processed_input = preprocess(input)
  -        # output = self.model(processed_input, scale)
  +        # processed_input = preprocess(image)
  +        # output = self.model(processed_image, scale)
           # return postprocess(output)
  
verification_info: {"golden_diff": "diff --git a/pkg/cli/init-templates/predict.py b/pkg/cli/init-templates/predict.py\n--- a/pkg/cli/init-templates/predict.py\n+++ b/pkg/cli/init-templates/predict.py\n@@ -11,12 +11,12 @@\n \n     def predict(\n         self,\n-        input: Path = Input(description=\"Grayscale input image\"),\n+        image: Path = Input(description=\"Grayscale input image\"),\n         scale: float = Input(\n             description=\"Factor to scale image by\", ge=0, le=10, default=1.5\n         ),\n     ) -> Path:\n         \"\"\"Run a single prediction on the model\"\"\"\n-        # processed_input = preprocess(input)\n-        # output = self.model(processed_input, scale)\n+        # processed_input = preprocess(image)\n+        # output = self.model(processed_image, scale)\n         # return postprocess(output)\n", "issue": "What should `cog predict` do if you don't pass an input name?\nThe syntax of `cog predict` is this:\r\n\r\n    cog predict -i image=@image.jpg\r\n\r\nBut you can also do this:\r\n\r\n    cog predict -i @image.jpg\r\n\r\nWhich implicitly means an input name of `input`. This is a neat short hand but a bit weird for a few reasons:\r\n\r\n- `input` is a Python built-in, so we should really be encouraging using that as a variable name.\r\n- It is a magic name.\r\n- For a sufficiently complex model, you probably don't want to call it `input`.\r\n\r\nWhat could we do that is better here? Maybe if you don't pass a name, it defaults to the _first_ input defined, rather than a magic name? This is vaguely backwards compatible, which is neat.\n", "before_files": [{"content": "# Prediction interface for Cog \u2699\ufe0f\n# https://github.com/replicate/cog/blob/main/docs/python.md\n\nfrom cog import BasePredictor, Input, Path\n\n\nclass Predictor(BasePredictor):\n    def setup(self):\n        \"\"\"Load the model into memory to make running multiple predictions efficient\"\"\"\n        # self.model = torch.load(\"./weights.pth\")\n\n    def predict(\n        self,\n        input: Path = Input(description=\"Grayscale input image\"),\n        scale: float = Input(\n            description=\"Factor to scale image by\", ge=0, le=10, default=1.5\n        ),\n    ) -> Path:\n        \"\"\"Run a single prediction on the model\"\"\"\n        # processed_input = preprocess(input)\n        # output = self.model(processed_input, scale)\n        # return postprocess(output)\n", "path": "pkg/cli/init-templates/predict.py"}], "after_files": [{"content": "# Prediction interface for Cog \u2699\ufe0f\n# https://github.com/replicate/cog/blob/main/docs/python.md\n\nfrom cog import BasePredictor, Input, Path\n\n\nclass Predictor(BasePredictor):\n    def setup(self):\n        \"\"\"Load the model into memory to make running multiple predictions efficient\"\"\"\n        # self.model = torch.load(\"./weights.pth\")\n\n    def predict(\n        self,\n        image: Path = Input(description=\"Grayscale input image\"),\n        scale: float = Input(\n            description=\"Factor to scale image by\", ge=0, le=10, default=1.5\n        ),\n    ) -> Path:\n        \"\"\"Run a single prediction on the model\"\"\"\n        # processed_input = preprocess(image)\n        # output = self.model(processed_image, scale)\n        # return postprocess(output)\n", "path": "pkg/cli/init-templates/predict.py"}]}