add log, better readme

app.py

@@ -8,9 +8,7 @@ from onnx_export import convert
 from huggingface_hub import HfApi, Repository
 
 
-# TODO: save stats about the Space?
-"""
-DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions"
+DATASET_REPO_URL = "https://huggingface.co/datasets/optimum/exporters"
 DATA_FILENAME = "data.csv"
 DATA_FILE = os.path.join("data", DATA_FILENAME)
 
@@ -19,7 +17,6 @@ HF_TOKEN = os.environ.get("HF_TOKEN")
 repo: Optional[Repository] = None
 if HF_TOKEN:
     repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN)
-"""
 
 def onnx_export(token: str, model_id: str, task: str) -> str:
     if token == "" or model_id == "":
@@ -37,11 +34,7 @@ def onnx_export(token: str, model_id: str, task: str) -> str:
         
         print("[commit_info]", commit_info)
 
-        return f"### Success 🔥 Yay! This model was successfully converted and a PR was open using your token, here: [{commit_info.pr_url}]({commit_info.pr_url})"
-    except Exception as e:
-        return f"### Error: {e}"
-        """
-        # save in a private dataset:
+        # save in a private dataset
         if repo is not None:
             repo.git_pull(rebase=True)
             with open(DATA_FILE, "a") as csvfile:
@@ -59,27 +52,39 @@ def onnx_export(token: str, model_id: str, task: str) -> str:
             print("[dataset]", commit_url)
 
         return f"### Success 🔥 Yay! This model was successfully converted and a PR was open using your token, here: {commit_info.pr_url}]({commit_info.pr_url})"
-        """
-
+    except Exception as e:
+        return f"### Error: {e}"
 
 DESCRIPTION = """
+<p align="center">
+    <img src="clean_hf_onnx.png"/>
+</p>
+
+This Space allows to automatically convert to ONNX transformers models hosted on the Hugging Face Hub. It opens a PR on the target model, and it is up to the owner of the original model
+to merge the PR to allow people to leverage the ONNX standard to share and use the model on a wide range of devices!
+
+Once converted, the model can for example be used in the [🤗 Optimum](https://huggingface.co/docs/optimum/) library following closely the transormers API.
+Check out [this guide](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models) to see how!
+
 The steps are the following:
 - Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
-- Input a model id from the Hub
+- Input a model id from the Hub (for example:)
 - If necessary, input the task for this model.
 - Click "Convert to ONNX"
 - That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR!
+
+Note: in case the model to convert is larger than 2 GB, it will be saved in a subfolder called `onnx/`. To load it from Optimum, the argument `subfolder="onnx"` should be provided.
 """
 
 demo = gr.Interface(
-    title="POC to convert any PyTorch model to ONNX",
+    title="Convert any PyTorch model to ONNX with 🤗 Optimum Exporters 🏎️",
     description=DESCRIPTION,
     allow_flagging="never",
     article="Check out the [Optimum repo on GitHub](https://github.com/huggingface/optimum)",
     inputs=[
         gr.Text(max_lines=1, label="Hugging Face token"),
-        gr.Text(max_lines=1, label="model_id"),
-        gr.Text(value="auto", max_lines=1, label="task (can be left blank, will auto-infer)")
+        gr.Text(max_lines=1, label="Model name", placeholder="textattack/distilbert-base-cased-CoLA"),
+        gr.Text(value="auto", max_lines=1, label="Task (can be left blank, will be automatically inferred)")
     ],
     outputs=[gr.Markdown(label="output")],
     fn=onnx_export,