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from pprint import pprint, pformat
import os
import gradio as gr
import click
from rasa.nlu.model import Interpreter
RASA_MODEL_PATH = "woz_nlu_agent/models/nlu"
interpreter = None
MODEL_TYPES = {
"Out-of-scope classifier": "oos",
"Intent classifier": "intent_transformer",
"Intent and Entity extractor": "rasa_intent_entity"
}
def predict(model_type, input):
if MODEL_TYPES[model_type] == "rasa_intent_entity":
return rasa_predict(input)
elif MODEL_TYPES[model_tyoe] == "oos":
return "TODO: out of scope"
elif MODEL_TYPES[model_type] == "intent_transformer":
return "TODO:: intent_transformer"
def rasa_predict(input):
def rasa_output(text):
message = str(text).strip()
result = interpreter.parse(message)
return result
response = rasa_output(input)
del response["response_selector"]
response["intent_ranking"] = response["intent_ranking"][:3]
if "id" in response["intent"]:
del response["intent"]["id"]
for i in response["intent_ranking"]:
if "id" in i:
del i["id"]
for e in response["entities"]:
if "extractor" in e:
del e["extractor"]
if "start" in e and "end" in e:
del e["start"]
del e["end"]
return pformat(response, indent=4)
def main():
global interpreter
print("Loading model...")
print(os.listdir("woz_nlu_agent/models/nlu"))
print(open("woz_nlu_agent/models/nlu/metadata.json", "r").read())
import json
print(json.load(open("woz_nlu_agent/models/nlu/metadata.json", "r")))
interpreter = Interpreter.load(RASA_MODEL_PATH)
print("Model loaded.")
iface = gr.Interface(fn=predict, inputs=[gr.inputs.Dropdown(MODEL_TYPES.keys()), "text"], outputs="text")
iface.launch()
if __name__ == "__main__":
main()
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