added convo_starter

src/text_classification/convo_starter_orgbot/local/Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+WORKDIR /app
+
+
+#install requirements
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Copy the rest of the application code to the working directory
+COPY . /app/
+EXPOSE 8000
+# Set the entrypoint for the container
+CMD ["hypercorn", "--bind", "0.0.0.0:8000", "api:app"]

src/text_classification/convo_starter_orgbot/local/README.md

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+## Grievance classification:
+
+
+### Purpose :
+Model to classify grievances into 3 buckets :
+- Label 0: 'General questions'
+- Label 1: 'Starter:  Hi, hello etc'
+
+
+
+### Testing the model deployment :  
+To run for testing just the Hugging Face deployment for grievence recognition, you can follow the following steps : 
+
+- Git clone the repo
+- Go to current folder location i.e. ``` cd /src/text_classification/flow_classification/local ```
+- Create docker image file and test the api:  
+```
+docker build -t testmodel .
+docker run -p 8000:8000 testmodel
+curl -X POST -H "Content-Type: application/json" -d '{"text": "Where is my money? "}' http://localhost:8000/
+```

src/text_classification/convo_starter_orgbot/local/__init__.py

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+from .request import ModelRequest
+from .request import Model

src/text_classification/convo_starter_orgbot/local/api.py

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+from model import Model
+from request import ModelRequest
+from quart import Quart, request
+import aiohttp
+
+app = Quart(__name__)
+
+model = None
+
+@app.before_serving
+async def startup():
+    app.client = aiohttp.ClientSession()
+    global model
+    model = Model(app)
+
+@app.route('/', methods=['POST'])
+async def embed():
+    global model
+    data = await request.get_json()
+    req = ModelRequest(**data)
+    return await model.inference(req)
+
+if __name__ == "__main__":
+    app.run()

src/text_classification/convo_starter_orgbot/local/model.py

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+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from request import ModelRequest
+
+class Model():
+    def __new__(cls, context):
+        cls.context = context
+        if not hasattr(cls, 'instance'):
+            cls.instance = super(Model, cls).__new__(cls)
+        model_name = "GautamR/convo_beginner_orgbot"
+        cls.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        cls.model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        cls.model.to(cls.device)
+        return cls.instance
+
+
+    async def inference(self,  request: ModelRequest):
+        inputs = self.tokenizer(request.text, return_tensors="pt")
+        inputs = {key: value.to(self.device) for key, value in inputs.items()}
+        with torch.no_grad():
+            logits = self.model(**inputs).logits
+        predicted_class_id = logits.argmax().item()
+        return self.model.config.id2label[predicted_class_id]

src/text_classification/convo_starter_orgbot/local/request.py

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+import requests
+import json
+
+
+class ModelRequest():
+    def __init__(self, text):
+        self.text = text
+
+    def to_json(self):
+        return json.dumps(self, default=lambda o: o.__dict__,
+                          sort_keys=True, indent=4)

src/text_classification/convo_starter_orgbot/local/requirements.txt

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+torch==2.0.1 --index-url https://download.pytorch.org/whl/cpu
+transformers
+quart
+aiohttp