initialize

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+import fasttext
+import numpy as np
+
+
+model = "cc.en.300.bin"
+model = fasttext.load_model(model)
+
+def get_cosine(a, b):
+    cosine_similarity = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+    return cosine_similarity
+
+
+def get_score(targets, answers, th):
+    th = float(th)
+
+    results = []
+    targets = targets.split(",")
+    target_len = len(targets)
+    answers = answers.split(",")
+
+    # th = 0.6
+    for i, target in enumerate(targets):
+        if target[0]==" ": target=target[1:]
+        # if i>2:break
+        target_vector = model.get_word_vector(target.lower())
+
+        cosine_list = []
+        # compare all of answer data
+        for answer in answers:
+            answer_vector = model.get_word_vector(answer.lower())
+            cosine = get_cosine(target_vector, answer_vector)
+            cosine_list.append(cosine)
+
+        # comparison is over threshold -> true else -> false
+        # results.append(max(cosine_list)>th)
+
+        # reject answer once it is used
+        if max(cosine_list)>th:
+            argmax = cosine_list.index(max(cosine_list))
+            results.append(max(cosine_list))
+            answers.remove(answers[argmax])
+
+        if len(answers)==0: break
+    if len(results)==0: return 0
+    result = int(sum(results) / len(results) * 100)
+    # result = sum(results) / target_len
+    return result
+
+
+
+app = FastAPI()
+
+
+@app.get("/ping")
+def read_root():
+    return {"ok"}
+
+
+@app.post("/scores/")
+def my_app(targets, answers, th=0.6):
+    return get_score(targets, answers, th)

requirements.txt

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+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+fasttext
+numpy