Avanzando y traduciendo

app.py

@@ -9,27 +9,21 @@ def request_pathname(files):
     return [[file.name, file.name.split('/')[-1]] for file in files]
 
 
-def validate_dataset(dataset, openapi):
+def validate_dataset(dataset):
     global docs
     docs = None  # clear it out if dataset is modified
     docs_ready = dataset.iloc[-1, 0] != ""
-    if docs_ready and type(openapi) is str and len(openapi) > 0:
-        return "✨Ready✨"
-    elif docs_ready:
-        return "⚠️Waiting for key..."
-    elif type(openapi) is str and len(openapi) > 0:
-        return "⚠️Waiting for documents..."
+    if docs_ready:
+        return "✨Listo✨"
     else:
-        return "⚠️Waiting for documents and key..."
+        return "⚠️Waiting for documents..."
 
 
-def do_ask(question, button, openapi, dataset, progress=gr.Progress()):
+def do_ask(question, button, dataset, progress=gr.Progress()):
     global docs
     docs_ready = dataset.iloc[-1, 0] != ""
-    if button == "✨Ready✨" and type(openapi) is str and len(openapi) > 0 and docs_ready:
+    if button == "✨Listo✨" and docs_ready:
         if docs is None:  # don't want to rebuild index if it's already built
-            import os
-            os.environ['OPENAI_API_KEY'] = openapi.strip()
             import paperqa
             docs = paperqa.Docs()
             # dataset is pandas dataframe
@@ -40,17 +34,21 @@ def do_ask(question, button, openapi, dataset, progress=gr.Progress()):
                 docs.add(row['filepath'], row['citation string'], key=key)
     else:
         return ""
-    progress(0, "Building Index...")
+    progress(0, "Construyendo índices...")
     docs._build_faiss_index()
-    progress(0.25, "Querying...")
+    progress(0.25, "Encolando...")
     result = docs.query(question)
-    progress(1.0, "Done!")
+    progress(1.0, "¡Hecho!")
     return result.formatted_answer, result.context
 
 
 with gr.Blocks() as demo:
     gr.Markdown("""
-    # Document Question and Answer
+    # Document Question and Answer adaptado al castellano por Pablo Ascorbe.
+
+    Este espacio ha sido clonado y adaptado de: https://huggingface.co/spaces/whitead/paper-qa
+
+    - Texto original:
 
     This tool will enable asking questions of your uploaded text or PDF documents.
     It uses OpenAI's GPT models and thus you must enter your API key below. This
@@ -60,15 +58,13 @@ with gr.Blocks() as demo:
     * [PaperQA](https://github.com/whitead/paper-qa) is the code used to build this tool.
     * [langchain](https://github.com/hwchase17/langchain) is the main library this tool utilizes.
 
-    ## Instructions
+    ## Instrucciones:
 
-    1. Enter API Key ([What is that?](https://openai.com/api/))
-    2. Upload your documents and modify citation strings if you want (to look prettier)
+    Adjunte su documento, ya sea en formato .txt o .pdf, y pregunte lo que desee.
+    
     """)
-    openai_api_key = gr.Textbox(
-        label="OpenAI API Key", placeholder="sk-...", type="password")
     uploaded_files = gr.File(
-        label="Your Documents Upload (PDF or txt)", file_count="multiple", )
+        label="Sus documentos subidos (PDF o txt)", file_count="multiple", )
     dataset = gr.Dataframe(
         headers=["filepath", "citation string"],
         datatype=["str", "str"],
@@ -76,23 +72,21 @@ with gr.Blocks() as demo:
         interactive=True,
         label="Documents and Citations"
     )
-    buildb = gr.Textbox("⚠️Waiting for documents and key...",
+    buildb = gr.Textbox("⚠️Esperando documentos...",
                         label="Status", interactive=False, show_label=True)
-    openai_api_key.change(validate_dataset, inputs=[
-                          dataset, openai_api_key], outputs=[buildb])
     dataset.change(validate_dataset, inputs=[
                    dataset, openai_api_key], outputs=[buildb])
     uploaded_files.change(request_pathname, inputs=[
                           uploaded_files], outputs=[dataset])
     query = gr.Textbox(
-        placeholder="Enter your question here...", label="Question")
-    ask = gr.Button("Ask Question")
-    gr.Markdown("## Answer")
-    answer = gr.Markdown(label="Answer")
-    with gr.Accordion("Context", open=False):
+        placeholder="Introduzca su pregunta aquí...", label="Pregunta")
+    ask = gr.Button("Pregunte")
+    gr.Markdown("## Respuesta")
+    answer = gr.Markdown(label="Respuesta")
+    with gr.Accordion("Contexto", open=False):
         gr.Markdown(
-            "### Context\n\nThe following context was used to generate the answer:")
-        context = gr.Markdown(label="Context")
+            "### Contexto\n\nEl siguiente contexto ha sido utilizado para generar la respuesta:")
+        context = gr.Markdown(label="Contexto")
     ask.click(fn=do_ask, inputs=[query, buildb,
                                  openai_api_key, dataset], outputs=[answer, context])