First upload to the repository.

.gitignore

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+__pycache__/

Dockerfile

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+FROM python:3.12-slim AS base
+
+RUN apt-get update && apt-get install -y git
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+COPY requirements.txt .
+
+RUN pip3 install -r requirements.txt
+
+COPY . .

main_service.py

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+import gradio as gr
+from utils_model import ModelFactory
+
+def retrieve_embeddings(input_text_query):
+    model_type = 'mock'
+    model_factory_obj = ModelFactory()
+    model = model_factory_obj.create_model(model_type)
+
+    query_embeddings = model.retrieve_embeddings(input_text_query)
+
+    return query_embeddings
+
+def build():
+    app = gr.Interface(fn=retrieve_embeddings, inputs="text", outputs="dataframe")
+
+    return app
+
+def run(app):
+    app.launch(server_name='0.0.0.0')
+
+app = build()
+run(app)

requirements.txt

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+gradio==5.37.0
+numpy==2.3.1
+pandas==2.3.1

utils_model.py

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+import numpy as np
+import pandas as pd
+# from sentence_transformers import SentenceTransformer
+
+class ModelFactory():
+
+    def __init__(self):
+        pass
+
+    def create_model(self, model_type):
+        model = None
+
+        if (model_type=='mock'):
+            model = MockModel()
+
+        # if (model_type=='all-MiniLM-L6-v2'):
+        #     model = MiniLM_L6_v2_Model()
+
+        return model
+
+class BaseModel():
+
+    def __init__(self):
+        pass
+
+    def retrieve_embeddings(self, input_text):
+        pass    
+
+class MockModel(BaseModel):
+
+    def __init__(self):
+        pass
+
+    def retrieve_embeddings(self, input_text):
+        random_embeddings = np.random.randint(256, size=(370))/256
+
+        return pd.DataFrame(random_embeddings)
+
+# class MiniLM_L6_v2_Model(BaseModel):
+    
+#     def __init__(self):
+#         self.model = SentenceTransformer('all-MiniLM-L6-v2')
+
+#     def retrieve_embeddings(self, input_text):
+#         embeddings = self.model.encode(input_text, batch_size=32)
+#         embeddings *= 255
+#         embeddings = embeddings.astype(np.uint8).tolist()
+
+#         return embeddings