first commit

app.py

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+import fasttext
+import gradio as gr
+from gradio import components as components
+import numpy as np
+
+model = "cc.en.300.bin"
+model = fasttext.load_model(model)
+
+def get_cosine(a, b):
+    # vectorize
+    # a = model.get_word_vector(a.lower())
+    # b = model.get_word_vector(b.lower())
+    cosine_similarity = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+    return cosine_similarity
+
+
+def get_score(targets, answers):
+
+    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 = sum(results)/len(results)
+    # result = sum(results) / target_len
+    return result
+
+
+def my_app(input1, input2):
+    return get_score(input1, input2)
+
+inputs = [
+    components.Textbox(label="Input 1"),
+    components.Textbox(label="Input 2")
+]
+output = components.Textbox(label="Output")
+
+app = gr.Interface(
+    fn=my_app, 
+    inputs=inputs,
+    outputs=output
+    )
+app.launch()

cc.en.300.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14c7167b130056944cbdc37b7451f055867fe9a4e3fed3bbc1ecc0e74f6763ca
+size 7237176312

requirements.txt

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+gradio
+fasttext