Create app_new.py
Browse files- app_new.py +75 -0
app_new.py
ADDED
|
@@ -0,0 +1,75 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
from transformers import pipeline
|
| 3 |
+
import json
|
| 4 |
+
import os
|
| 5 |
+
from openai import OpenAI
|
| 6 |
+
|
| 7 |
+
api_key = os.getenv("OPENAI_KEY")
|
| 8 |
+
client = OpenAI(api_key=api_key)
|
| 9 |
+
modelx = 'gpt-3.5-turbo-0125'
|
| 10 |
+
|
| 11 |
+
def generacion_llm(texto_input):
|
| 12 |
+
# Define the system and user messages
|
| 13 |
+
formato_json = '''
|
| 14 |
+
{
|
| 15 |
+
"reto": " ",
|
| 16 |
+
"dudas": " ",
|
| 17 |
+
"preguntas": " ",
|
| 18 |
+
"expectativas": " "
|
| 19 |
+
}
|
| 20 |
+
'''
|
| 21 |
+
|
| 22 |
+
mensaje_sistema = (
|
| 23 |
+
"Eres un experto en identificar aspectos descriptivos de las razones "
|
| 24 |
+
"por las cuales un usuario necesita asesor铆a para implementar retos "
|
| 25 |
+
"que involucren inteligencia artificial de varios tipos."
|
| 26 |
+
)
|
| 27 |
+
|
| 28 |
+
mensaje_usuario = (
|
| 29 |
+
f"Analizar el texto mostrado al final, buscando identificar los siguientes "
|
| 30 |
+
f"extractos en el formato JSON: {formato_json}\n\nTexto a Analizar: {texto_input}"
|
| 31 |
+
)
|
| 32 |
+
|
| 33 |
+
# Call OpenAI API
|
| 34 |
+
response = openai.ChatCompletion.create(
|
| 35 |
+
model=modelx,
|
| 36 |
+
messages=[
|
| 37 |
+
{"role": "system", "content": mensaje_sistema},
|
| 38 |
+
{"role": "user", "content": mensaje_usuario}
|
| 39 |
+
],
|
| 40 |
+
temperature=0.8,
|
| 41 |
+
max_tokens=300,
|
| 42 |
+
top_p=1,
|
| 43 |
+
frequency_penalty=0,
|
| 44 |
+
presence_penalty=0
|
| 45 |
+
)
|
| 46 |
+
|
| 47 |
+
# Extract the generated text from the response
|
| 48 |
+
texto_respuesta = response["choices"][0]["message"]["content"]
|
| 49 |
+
|
| 50 |
+
# Ensure the response is valid JSON
|
| 51 |
+
try:
|
| 52 |
+
parsed_json = json.loads(texto_respuesta) # Attempt to parse the response
|
| 53 |
+
except json.JSONDecodeError:
|
| 54 |
+
return None, "Error: El modelo no devolvi贸 un JSON v谩lido. Por favor revise el input."
|
| 55 |
+
|
| 56 |
+
# Save the parsed JSON as a downloadable file
|
| 57 |
+
json_filename = "resultado.json"
|
| 58 |
+
with open(json_filename, "w", encoding="utf-8") as f:
|
| 59 |
+
json.dump(parsed_json, f, ensure_ascii=False, indent=4)
|
| 60 |
+
|
| 61 |
+
return parsed_json, json_filename
|
| 62 |
+
|
| 63 |
+
# Define Gradio app
|
| 64 |
+
interface = gr.Interface(
|
| 65 |
+
fn=generacion_llm,
|
| 66 |
+
inputs=gr.Textbox(label="Ingrese su texto para analizar"),
|
| 67 |
+
outputs=[
|
| 68 |
+
gr.JSON(label="Resultado JSON"), # Show the generated JSON
|
| 69 |
+
gr.File(label="Descargar JSON") # Allow the user to download the JSON
|
| 70 |
+
],
|
| 71 |
+
title="Extractor de Texto a JSON",
|
| 72 |
+
description="Ingrese el texto para analizar y extraer informaci贸n en un formato JSON predefinido."
|
| 73 |
+
)
|
| 74 |
+
|
| 75 |
+
interface.launch()
|