Update app.py

app.py

@@ -1,21 +1,87 @@
-import gradio
+import gradio as gr
 import gradio_client
+import inspect
+from gradio import routes
+from typing import List, Type
 
-gradio_client = GrClient("https://ldhldh-polyglot-ko-1-3b-peft-demo.hf.space/")
-
-def my_inference_function(name):
-  return "Hello " + name + "!"
-
-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="© Tom Söderlund 2022"
-)
-gradio_interface.launch()
+# 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
+
+# App code
+def mbti(x):
+
+    return 0
+    
+def chat(x):
+    result = gradio_client.predict(
+        user_input,# str representing input in 'User input' Textbox component
+		0.9,	# float, representing input in 'Top-p (nucleus sampling)' Slider component
+		50,	# int, representing input in 'Top-k (nucleus sampling)' Slider component
+		0.7,	# float, representing input in 'Temperature' Slider component
+		25,	# int, representing input in 'Max New Tokens' Slider component
+		1.2,	# float, representing input in 'repetition_penalty' Slider component
+		fn_index=0
+    )
+    return result
+
+def yn(x):
+    result = gradio_client.predict(
+				text,	# str representing input in 'User input' Textbox component
+				fn_index=2
+    )
+    return result
+
+    
+
+with gr.Blocks() as blk:
+    gr.Markdown("# Gradio Blocks (3.0) with REST API")
+    t = gr.Textbox()
+    c = gr.Button("mbti")
+    b = gr.Button("chat")
+    a = gr.Button("yn")
+    o = gr.Textbox()
+    c.click(mbti, inputs=[t], outputs=[o])
+    b.click(chat, inputs=[t], outputs=[o])
+    a.click(yn, inputs=[t], outputs=[o])
+    gr.Markdown("""
+## API
+Can select which function to use by passing in `fn_index`:
+```python
+import requests
+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()
+```
+Or using cURL
+```
+$ 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}'
+```""")
+
+ifa = gr.Interface(lambda: None, inputs=[t], outputs=[o])
+
+blk.input_components = ifa.input_components
+blk.output_components = ifa.output_components
+blk.examples = None
+blk.predict_durations = []
+
+bapp = blk.launch()