Upload 4 files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModel
+import torch
+from sentence_transformers import SentenceTransformer
+
+
+# Load your model with trust_remote_code=True
+model = SentenceTransformer("truong1301/bi-encode-HG-DOCS", trust_remote_code=True)
+
+# Define a function to get embeddings
+def get_embedding(text):
+    with torch.no_grad():
+        embeddings = model.encode(text)
+    return embeddings
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=get_embedding,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="json",
+    title="Embedding Generator",
+    description="Get embeddings using ICTuniverse/tuned-bi-encoder"
+)
+
+# Launch the Gradio app
+iface.launch()

huggingface.yml

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+sdk: docker

requirements.txt

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+fastapi
+torch
+sentence-transformers
+uvicorn
+gradio