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import gradio as gr
from transformers import pipeline
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Dict
# Initialize the NER pipeline
ner_model = pipeline("ner", grouped_entities=True)
# Define the FastAPI app
app = FastAPI()
# Define the request and response models for the API
class NERRequest(BaseModel):
text: str
class Entity(BaseModel):
entity_group: str
start: int
end: int
score: float
word: str
class NERResponse(BaseModel):
entities: List[Entity]
@app.post("/ner", response_model=NERResponse)
def get_entities(request: NERRequest):
# Use the NER model to detect entities
entities = ner_model(request.text)
# Convert entities to the response model
response_entities = [Entity(**entity) for entity in entities]
return NERResponse(entities=response_entities)
# Define the Gradio interface function
def ner_demo(text):
entities = ner_model(text)
return {"entities": entities}
# Create the Gradio interface
iface = gr.Interface(
fn=ner_demo,
inputs=gr.Textbox(lines=10, placeholder="Enter text here..."),
outputs=gr.JSON(),
title="Named Entity Recognition",
description="Enter text to extract named entities using a NER model."
)
# Launch the Gradio interface
iface.launch(share=True)
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