Add API support

gradio_app.py

@@ -7,6 +7,31 @@ import torch
 from transformers import pipeline, set_seed
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import logging
+
+
+# Monkey patch
+import inspect
+from gradio import routes
+from typing import List, Type
+
+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
+
 logger = logging.getLogger()
 logger.addHandler(logging.StreamHandler())
 
@@ -149,27 +174,18 @@ class TextGeneration:
                     generated_text = generated_text.replace(text, "", 1).strip()
                 if generated_text:
                     return (
-                        text,
                         text + " " + generated_text,
                         [(text, None), (generated_text, "BERTIN")]
                     )
             if not generated_text:
                 return (
-                    "",
                     "",
                     [("Tras 10 intentos BERTIN no generó nada. Pruebe cambiando las opciones.", "ERROR")]
                 )
         return (
-            "",
             "",
             [("Debe escribir algo primero.", "ERROR")]
         )
-            # return (text + " " + generated_text,
-            #     f'<p class="ltr ltr-box">'
-            #     f'<span class="result-text">{text} <span>'
-            #     f'<span class="result-text generated-text">{generated_text}</span>'
-            #     f'</p>'
-            # )
 
 
 #@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
@@ -234,7 +250,7 @@ def chat_with_gpt(user, agent, context, user_message, history, max_length, top_k
             break
     context += history_context
     for _ in range(5):
-        response = generator.generate(f"{context}\n\n{user}: {message}.\n", generation_kwargs)[1]
+        response = generator.generate(f"{context}\n\n{user}: {message}.\n", generation_kwargs)[0]
         if DEBUG:
             print("\n-----" + response + "-----\n")
         response = response.split("\n")[-1]
@@ -325,9 +341,9 @@ with gr.Blocks() as demo:
                         output = gr.HighlightedText(label="Resultado", combine_adjacent=True, color_map={"BERTIN": "green", "ERROR": "red"})
                     with gr.Row():
                         generate_btn = gr.Button("Generar")
-                        generate_btn.click(complete_with_gpt, inputs=[textbox, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[textbox, hidden, output])
+                        generate_btn.click(complete_with_gpt, inputs=[textbox, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[hidden, output])
                         expand_btn = gr.Button("Añadir")
-                        expand_btn.click(expand_with_gpt, inputs=[hidden, textbox, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[textbox, hidden, output])
+                        expand_btn.click(expand_with_gpt, inputs=[hidden, textbox, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[hidden, output])
 
                         edit_btn = gr.Button("Editar", variant="secondary")
                         edit_btn.click(lambda x: (x, "", []), inputs=[hidden], outputs=[textbox, hidden, output])
@@ -346,10 +362,13 @@ with gr.Blocks() as demo:
                     with gr.Row():
                         message = gr.Textbox(placeholder="Escriba aquí su mensaje y pulse 'Enviar'", show_label=False)
                         chat_btn = gr.Button("Enviar")
-                    chat_btn.click(chat_with_gpt, inputs=[agent, user, context, message, history, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[chatbot, history, message])
+                    chat_btn.click(chat_with_gpt, inputs=[agent, user, context, message, history, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[chatbot, history, message]))
     gr.Markdown(FOOTER)
 
-
+with gr.Interface(lambda: None, inputs=["text", max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[hidden, output]) as iface:
+    demo.examples = None
+    demo.predict_durations = []
+    demo.input_components = iface.input_components
+    demo.output_components = iface.output_components
 
 demo.launch()
-# gr.Interface(complete_with_gpt, inputs=[textbox, max_length, top_k, top_p, temperature, do_sample, do_clean], outputs=[hidden, output]).launch()