Upload folder using huggingface_hub

Dockerfile

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+
+FROM python:3.9
+WORKDIR /app
+RUN apt-get update && apt-get install -y build-essential python3-dev && rm -rf /var/lib/apt/lists/*
+RUN pip install --no-cache-dir llama-cpp-python streamlit huggingface_hub
+# Download the model file during build
+RUN python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='gijl/ai', filename='brain_map_v3_f16.gguf', local_dir='.')"
+COPY . .
+EXPOSE 7860
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

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+
+import streamlit as st
+from llama_cpp import Llama
+import os
+
+st.set_page_config(page_title='Brain Map AI - GGUF Engine', layout='wide')
+st.title('🚀 Brain Map AI v3.0 (GGUF)')
+st.markdown('**Engine:** llama.cpp (High Performance C++)')
+
+@st.cache_resource
+def load_gguf():
+    # In a real Space, we would download from the repo
+    # For this demo, we assume the file is mapped or downloaded
+    model_path = 'brain_map_v3_f16.gguf'
+    return Llama(model_path=model_path, n_ctx=512, n_threads=2)
+
+try:
+    llm = load_gguf()
+    st.success('GGUF Engine Initialized')
+except Exception as e:
+    st.error(f'Initialization Error: {e}')
+
+query = st.text_input('Clinical Inquiry:')
+if st.button('Compute'):
+    if query:
+        with st.spinner('Processing via llama.cpp...'):
+            output = llm(f'Q: {query} A:', max_tokens=150, stop=['Q:'], echo=False)
+            st.markdown('### Result:')
+            st.write(output['choices'][0]['text'])

requirements.txt

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+llama-cpp-python
+streamlit
+huggingface_hub