update description

app.py

@@ -47,8 +47,9 @@ def process_model(
     model_name = model_id.split("/")[-1]
     username = whoami(oauth_token.token)["name"]
     new_repo_id = f"{username}/{model_name}-openvino-{dtype}"
-
     task = TasksManager.infer_task_from_model(model_id)
+    library_name = TasksManager.infer_library_from_model(model_id)
+
     if task not in _HEAD_TO_AUTOMODELS:
         raise ValueError(
             f"The task '{task}' is not supported, only {_HEAD_TO_AUTOMODELS.keys()} tasks are supported"
@@ -66,7 +67,6 @@ def process_model(
     export = len(ov_files) == 0
 
     is_int8 = dtype == "int8"
-    library_name = TasksManager.infer_library_from_model(model_id)
     if library_name == "diffusers":
         quant_method = "hybrid"
     elif not is_int8:
@@ -160,6 +160,12 @@ def process_model(
             shutil.rmtree(folder, ignore_errors=True)
 
 
+DESCRIPTION = """
+This Space uses [Optimum Intel](https://huggingface.co/docs/optimum/main/en/intel/openvino/optimization) to automatically apply NNCF weight only quantization on a model hosted on the [Hub](https://huggingface.co/models) and convert it to the [OpenVINO format](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) if not already.
+
+The resulting model will then be pushed under your HF user namespace. For now we only support conversion for models that are hosted on public repositories.
+"""
+
 model_id = HuggingfaceHubSearch(
     label="Hub Model ID",
     placeholder="Search for model id on the hub",
@@ -227,7 +233,7 @@ interface = gr.Interface(
         gr.Markdown(label="output"),
     ],
     title="Quantize your model with NNCF",
-    description="This space takes a model, converts it to the OpenVINO format and applies NNCF weight only quantization. The resulting model will then be pushed on the Hub under your HF user namespace",
+    description=DESCRIPTION,
     api_name=False,
 )