improve service names (#54)

- embedding-server => model_server
- public-types => public_types
- chatbot-ui => chatbot_ui
- function-calling => function_calling

embedding-server/app/main.py

@@ -1,145 +0,0 @@
-import random
-from fastapi import FastAPI, Response, HTTPException
-from pydantic import BaseModel
-from load_models import load_ner_models, load_transformers, load_zero_shot_models
-from datetime import date, timedelta
-import string
-
-transformers = load_transformers()
-ner_models = load_ner_models()
-zero_shot_models = load_zero_shot_models()
-
-app = FastAPI()
-
-class EmbeddingRequest(BaseModel):
-  input: str
-  model: str
-
-@app.get("/healthz")
-async def healthz():
-    return {
-        "status": "ok"
-    }
-
-@app.get("/models")
-async def models():
-    models = []
-
-    for model in transformers.keys():
-        models.append({
-            "id": model,
-            "object": "model"
-        })
-
-    return {
-        "data": models,
-        "object": "list"
-    }
-
-@app.post("/embeddings")
-async def embedding(req: EmbeddingRequest, res: Response):
-    if req.model not in transformers:
-        raise HTTPException(status_code=400, detail="unknown model: " + req.model)
-
-    embeddings = transformers[req.model].encode([req.input])
-
-    data = []
-
-    for embedding in embeddings.tolist():
-        data.append({
-            "object": "embedding",
-            "embedding": embedding,
-            "index": len(data)
-        })
-
-    usage = {
-        "prompt_tokens": 0,
-        "total_tokens": 0,
-    }
-    return {
-        "data": data,
-        "model": req.model,
-        "object": "list",
-        "usage": usage
-    }
-
-class NERRequest(BaseModel):
-  input: str
-  labels: list[str]
-  model: str
-
-
-@app.post("/ner")
-async def ner(req: NERRequest, res: Response):
-    if req.model not in ner_models:
-        raise HTTPException(status_code=400, detail="unknown model: " + req.model)
-
-    model = ner_models[req.model]
-    entities = model.predict_entities(req.input, req.labels)
-
-    return {
-        "data": entities,
-        "model": req.model,
-        "object": "list",
-    }
-
-class ZeroShotRequest(BaseModel):
-  input: str
-  labels: list[str]
-  model: str
-
-
-def remove_punctuations(s, lower=True):
-    s = s.translate(str.maketrans(string.punctuation, " " * len(string.punctuation)))
-    s = " ".join(s.split())
-    if lower:
-        s = s.lower()
-    return s
-
-
-@app.post("/zeroshot")
-async def zeroshot(req: ZeroShotRequest, res: Response):
-    if req.model not in zero_shot_models:
-        raise HTTPException(status_code=400, detail="unknown model: " + req.model)
-
-    classifier = zero_shot_models[req.model]
-    labels_without_punctuations = [remove_punctuations(label) for label in req.labels]
-    predicted_classes = classifier(req.input, candidate_labels=labels_without_punctuations, multi_label=True)
-    label_map = dict(zip(labels_without_punctuations, req.labels))
-
-    orig_map = [label_map[label] for label in predicted_classes["labels"]]
-    final_scores = dict(zip(orig_map, predicted_classes["scores"]))
-    predicted_class = label_map[predicted_classes["labels"][0]]
-
-    return {
-        "predicted_class": predicted_class,
-        "predicted_class_score": final_scores[predicted_class],
-        "scores": final_scores,
-        "model": req.model,
-    }
-
-
-class WeatherRequest(BaseModel):
-  city: str
-
-
-@app.post("/weather")
-async def weather(req: WeatherRequest, res: Response):
-
-    weather_forecast = {
-        "city": req.city,
-        "temperature": [],
-        "unit": "F",
-    }
-    for i in range(7):
-       min_temp = random.randrange(50,90)
-       max_temp = random.randrange(min_temp+5, min_temp+20)
-       weather_forecast["temperature"].append({
-           "date": str(date.today() + timedelta(days=i)),
-           "temperature": {
-              "min": min_temp,
-              "max": max_temp
-           }
-       })
-
-    return weather_forecast