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import gradio as gr
import json
from huggingface_hub import InferenceClient
client = InferenceClient(
"mistralai/Mistral-7B-Instruct-v0.1"
)
rag_text = "Este es el texto RAG"
prompt_template_text = "Este es el texto del template de prompt"
def format_prompt(message):
prompt = "<s>"
prompt += f"[INST] {message} [/INST]"
return prompt
def generate(prompt):
generate_kwargs = dict(
temperature=0.9,
max_new_tokens=1024,
top_p=0.95,
repetition_penalty=1.0,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt)
output = client.text_generation(formatted_prompt, **generate_kwargs)
return output
def process_input(text, rag, prompt_template):
prompt = text
if rag:
prompt += rag_text
if prompt_template:
prompt += prompt_template_text
output = generate(prompt)
# Convertimos el generador en una cadena JSON
json_str = ''.join(output)
# Convertimos la cadena JSON en un objeto JSON
json_obj = json.loads(json_str)
# Generamos el archivo JSON
with open('output.json', 'w') as f:
json.dump(json_obj, f)
return output
def create_interface():
# Definimos los componentes de la interfaz
input_text = gr.Textbox(label="Input")
rag_checkbox = gr.Checkbox(label="RAG")
prompt_template = gr.Checkbox(label="PromptTemplate")
output_text = gr.Textbox(label="Output")
classification_types_checkboxes = gr.CheckboxGroup(label="Clasificacion Tipo")
# Definimos la función que se ejecutará cuando se envíe la entrada
def fn(text, rag, prompt_template):
output = process_input(text, rag, prompt_template)
with open('output.json', 'r') as f:
data = json.load(f)
classification_types = [item['clasificacion_tipo'] for item in data]
classification_types_options = [(option, option) for option in classification_types]
classification_types_checkboxes = gr.CheckboxGroup(label="Clasificacion Tipo", choices=classification_types_options, interactive = True)
return output, classification_types_checkboxes
examples = [
["Ejemplo de texto", True, False],
["Otro ejemplo", False, True]
]
# Creamos la interfaz
iface = gr.Interface(
fn=fn,
inputs=[input_text, rag_checkbox, prompt_template],
outputs=[output_text, classification_types_checkboxes],
examples=examples
)
return iface
iface = create_interface()
iface.launch() |