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from pprint import pprint, pformat |
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import os |
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import gradio as gr |
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import click |
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import torch |
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from rasa.nlu.model import Interpreter |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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RASA_MODEL_PATH = "woz_nlu_agent/models/nlu-lookup-1" |
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interpreter = Interpreter.load(RASA_MODEL_PATH) |
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OOS_MODEL= "msamogh/autonlp-cai-out-of-scope-649919116" |
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tokenizer = AutoTokenizer.from_pretrained(OOS_MODEL) |
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model = AutoModelForSequenceClassification.from_pretrained(OOS_MODEL) |
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MODEL_TYPES = { |
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"Out-of-scope classifier": "oos", |
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"Intent classifier": "intent_transformer", |
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"Intent and Entity extractor": "rasa_intent_entity" |
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} |
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def predict(model_type, input): |
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if MODEL_TYPES[model_type] == "rasa_intent_entity": |
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return rasa_predict(input) |
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elif MODEL_TYPES[model_type] == "oos": |
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return oos_predict(input) |
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elif MODEL_TYPES[model_type] == "intent_transformer": |
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return "WIP: intent_transformer" |
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def oos_predict(input): |
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inputs = tokenizer(input, return_tensors="pt") |
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outputs = model(**inputs).logits |
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outputs = torch.softmax(torch.tensor(outputs), dim=-1)[0] |
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return str({"In scope": outputs[1], "Out of scope": outputs[0]}) |
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def rasa_predict(input): |
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def rasa_output(text): |
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message = str(text).strip() |
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result = interpreter.parse(message) |
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return result |
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response = rasa_output(input) |
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del response["response_selector"] |
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response["intent_ranking"] = response["intent_ranking"][:3] |
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if "id" in response["intent"]: |
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del response["intent"]["id"] |
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for i in response["intent_ranking"]: |
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if "id" in i: |
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del i["id"] |
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for e in response["entities"]: |
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if "extractor" in e: |
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del e["extractor"] |
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if "start" in e and "end" in e: |
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del e["start"] |
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del e["end"] |
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return pformat(response, indent=4) |
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def main(): |
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iface = gr.Interface(fn=predict, inputs=[gr.inputs.Dropdown(list(MODEL_TYPES.keys())), "text"], outputs="text") |
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iface.launch() |
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if __name__ == "__main__": |
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main() |
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