init application

Signed-off-by: Ilham Syahid S <ilhamsyahids@gmail.com>

app.py

@@ -0,0 +1,88 @@
+import requests
+import gradio as gr
+from numpy import dot
+from numpy.linalg import norm
+from os import environ
+
+num_of_sentences = 2
+
+endpoint = environ.get("ENDPOINT", "")
+api_key = environ.get("API_KEY", "")
+
+
+def cos_sim(a, b):
+    return dot(a, b) / (norm(a) * norm(b))
+
+
+def get_embeddings(model, text):
+    # make POST request to the endpoint
+    response = requests.post(
+        f"{endpoint}/{model}",
+        json={"text": text},
+        headers={"Authorization": "Bearer " + api_key},
+    )
+
+    return response.json()["vector"]
+
+
+def calculate_similarities(model, text, *sentences):
+    # get embeddings for the input text
+    text_embedding = get_embeddings(model, text)
+
+    # get embeddings for the input sentences
+    sentences_embeddings = [get_embeddings(model, sentence) for sentence in sentences]
+
+    # calculate cosine similarity between the input text and the input sentences
+    similarities = {}
+    for sentence, sentence_embedding in zip(sentences, sentences_embeddings):
+        similarities[sentence] = cos_sim(text_embedding, sentence_embedding)
+
+    return similarities
+
+
+demo = gr.Blocks()
+
+with demo:
+    with gr.Row():
+        with gr.Column():
+            model = gr.inputs.Radio(
+                ["roberta", "ada"], default="roberta", label="Model"
+            )
+
+            text = gr.Textbox(lines=3, label="Input Text")
+
+            inp_sentences = [
+                gr.Textbox(lines=3, label="Sentence " + str(i + 1))
+                for i in range(num_of_sentences)
+            ]
+            btn = gr.Button(text="Submit")
+
+        with gr.Column():
+            output = gr.Label(label="Output", show_label=False)
+
+            # submit btn
+            btn.click(
+                calculate_similarities,
+                inputs=[model, text, *inp_sentences],
+                outputs=[output],
+            )
+
+    gr.Examples(
+        examples=[
+            ["roberta", "This is happy person", "هذا شخص سعيد", "هذه قطة سعيدة"],
+            ["roberta", "car", "camry", "toyota"],
+            ["roberta", "هذا شخص سعيد", "هذه قطة سعيدة", "This is happy person"],
+            ["roberta", "ihpone for sale", "iphone for sale", "camry for sale"],
+            ["ada", "camry", "toy", "toyota"],
+            ["ada", "This is happy person", "هذا شخص سعيد", "هذه قطة سعيدة"],
+            ["ada", "هذا شخص سعيد", "هذه قطة سعيدة", "This is happy person"],
+            ["ada", "ihpone for sale", "iphone for sale", "camry for sale"],
+        ],
+        inputs=[model, text, *inp_sentences],
+        outputs=output,
+        fn=calculate_similarities,
+        # cache_examples=True,
+    )
+
+if __name__ == "__main__":
+    demo.launch()