Feat: custom url and with fast api
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							|  | @ -21,6 +21,7 @@ from langchain.embeddings import HuggingFaceEmbeddings | |||
| import pandas as pd | ||||
| import sqlite3 | ||||
| from sentence_transformers import SentenceTransformer | ||||
| from fastapi import FastAPI | ||||
| #from cleantext import clean | ||||
| import re | ||||
| model_name = 'hiiamsid/sentence_similarity_spanish_es' | ||||
|  | @ -32,6 +33,13 @@ hf = HuggingFaceEmbeddings( | |||
|     encode_kwargs=encode_kwargs | ||||
| ) | ||||
| 
 | ||||
| CUSTOM_PATH = "/angela" | ||||
| app = FastAPI() | ||||
| 
 | ||||
| @app.get("/") | ||||
| def read_main(): | ||||
|     return {"message": "This is your main app"} | ||||
| 
 | ||||
| 
 | ||||
| def loadModels(): | ||||
|     #model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin") | ||||
|  | @ -124,7 +132,13 @@ with gr.Blocks() as demo: | |||
|     # metrica3=gr.Textbox(label="metrica3") | ||||
|     Enviar_btn = gr.Button("Responder") | ||||
| 
 | ||||
|     Enviar_btn.click(fn=QARequest, inputs=[Pregunta,filtred], outputs=[Respuesta,id], api_name="Angela") #  | ||||
|     Enviar_btn.click(fn=QARequest, inputs=[Pregunta,filtred], outputs=[Respuesta,id], api_name="api_angela") #  | ||||
| 
 | ||||
| demo.launch() # | ||||
| #demo.launch(root_path="angela") # | ||||
| 
 | ||||
| gradio_app = gr.routes.App.create_app(demo) | ||||
| 
 | ||||
| app.mount(CUSTOM_PATH, gradio_app) | ||||
| 
 | ||||
| #app = demo.mount_gradio_app(app, io, path=CUSTOM_PATH) | ||||
| 
 | ||||
|  |  | |||
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