test new approach

app.py

@@ -1,88 +1,89 @@
-import gradio as gr
-import inspect
-from gradio import routes
-from typing import List, Type
+import gradio
 
-# Monkey patch
-def get_types(cls_set: List[Type], component: str):
-    docset = []
-    types = []
-    if component == "input":
-        for cls in cls_set:
-            doc = inspect.getdoc(cls)
-            doc_lines = doc.split("\n")
-            docset.append(doc_lines[1].split(":")[-1])
-            types.append(doc_lines[1].split(")")[0].split("(")[-1])
-    else:
-        for cls in cls_set:
-            doc = inspect.getdoc(cls)
-            doc_lines = doc.split("\n")
-            docset.append(doc_lines[-1].split(":")[-1])
-            types.append(doc_lines[-1].split(")")[0].split("(")[-1])
-    return docset, types
-routes.get_types = get_types
+def my_inference_function(name):
+  return "Hello " + name + "!"
 
-# App code
-def hallo(x):
-    return f"Hallo, {x}"
+gradio_interface = gradio.Interface(
+  fn=my_inference_function,
+  inputs="text",
+  outputs="text",
+  examples=[
+    ["Jill"],
+    ["Sam"]
+  ],
+  title="REST API with Gradio and Huggingface Spaces",
+  description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
+  article="Test 2023"
+)
 
-def hadet(x):
-    return f"Hadet, {x}"
+gradio_interface.launch()
 
-with gr.Blocks() as blk:
-    # gr.Markdown("# Gradio Blocks (3.0) with REST API")
-    t = gr.Textbox()
-    b = gr.Button("Hallo")
-    a = gr.Button("Hadet")
-    o = gr.Textbox()
-    b.click(hallo, inputs=[t], outputs=[o])
-    a.click(hadet, inputs=[t], outputs=[o])
-    # gr.Markdown("""
-    #   ## API
-    #   Can select which function to use by passing in `fn_index`:
-    #   ```python
-    #   import requests
+# import gradio as gr
+# import inspect
+# from gradio import routes
+# from typing import List, Type
 
-    #   requests.post(
-    #       url="https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/", json={"data": ["Jessie"], "fn_index": 0}
-    #   ).json()
-    #   requests.post(
-    #       url="https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/", json={"data": ["Jessie"], "fn_index": 1}
-    #   ).json()
-    #   ```
+# # Monkey patch
+# def get_types(cls_set: List[Type], component: str):
+#     docset = []
+#     types = []
+#     if component == "input":
+#         for cls in cls_set:
+#             doc = inspect.getdoc(cls)
+#             doc_lines = doc.split("\n")
+#             docset.append(doc_lines[1].split(":")[-1])
+#             types.append(doc_lines[1].split(")")[0].split("(")[-1])
+#     else:
+#         for cls in cls_set:
+#             doc = inspect.getdoc(cls)
+#             doc_lines = doc.split("\n")
+#             docset.append(doc_lines[-1].split(":")[-1])
+#             types.append(doc_lines[-1].split(")")[0].split("(")[-1])
+#     return docset, types
+# routes.get_types = get_types
 
-    #   Or using cURL
+# # App code
+# def hallo(x):
+#     return f"Hallo, {x}"
 
-    #   ```
-    #   $ curl -X POST https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/ -H 'Content-Type: application/json' -d '{"data": ["Jessie"], "fn_index": 0}'
-    #   $ curl -X POST https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/ -H 'Content-Type: application/json' -d '{"data": ["Jessie"], "fn_index": 1}'
-    # ```""")
+# def hadet(x):
+#     return f"Hadet, {x}"
 
-ifa = gr.Interface(lambda: None, inputs=[t], outputs=[o])
+# with gr.Blocks() as blk:
+#     # gr.Markdown("# Gradio Blocks (3.0) with REST API")
+#     t = gr.Textbox()
+#     b = gr.Button("Hallo")
+#     a = gr.Button("Hadet")
+#     o = gr.Textbox()
+#     b.click(hallo, inputs=[t], outputs=[o])
+#     a.click(hadet, inputs=[t], outputs=[o])
+#     # gr.Markdown("""
+#     #   ## API
+#     #   Can select which function to use by passing in `fn_index`:
+#     #   ```python
+#     #   import requests
 
-blk.input_components = ifa.input_components
-blk.output_components = ifa.output_components
-blk.examples = None
-blk.predict_durations = []
+#     #   requests.post(
+#     #       url="https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/", json={"data": ["Jessie"], "fn_index": 0}
+#     #   ).json()
+#     #   requests.post(
+#     #       url="https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/", json={"data": ["Jessie"], "fn_index": 1}
+#     #   ).json()
+#     #   ```
 
-bapp = blk.launch()
+#     #   Or using cURL
 
-# import gradio
+#     #   ```
+#     #   $ curl -X POST https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/ -H 'Content-Type: application/json' -d '{"data": ["Jessie"], "fn_index": 0}'
+#     #   $ curl -X POST https://hf.space/embed/versae/gradio-blocks-rest-api/+/api/predict/ -H 'Content-Type: application/json' -d '{"data": ["Jessie"], "fn_index": 1}'
+#     # ```""")
 
-# def my_inference_function(name):
-#   return "Hello " + name + "!"
+# ifa = gr.Interface(lambda: None, inputs=[t], outputs=[o])
 
-# gradio_interface = gradio.Interface(
-#   fn=my_inference_function,
-#   inputs="text",
-#   outputs="text",
-#   examples=[
-#     ["Jill"],
-#     ["Sam"]
-#   ],
-#   title="REST API with Gradio and Huggingface Spaces",
-#   description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
-#   article="Test 2023"
-# )
+# blk.input_components = ifa.input_components
+# blk.output_components = ifa.output_components
+# blk.examples = None
+# blk.predict_durations = []
+
+# bapp = blk.launch()
 
-# gradio_interface.launch()