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Dockerfile

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+FROM python:3.10-slim
+
+# System dependencies
+RUN apt-get update && \
+    apt-get install -y build-essential git wget curl && \
+    rm -rf /var/lib/apt/lists/*
+
+# Create app directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+COPY app.py .
+
+# Expose Streamlit port
+EXPOSE 7860
+
+# Streamlit settings
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+CMD ["streamlit", "run", "app.py"]

README.md

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----
-title: Medical Chatbot
-emoji: 📚
-colorFrom: purple
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🩺 Medical Chatbot - BioMistral
+
+This is a doctor-style medical chatbot running on [BioMistral-7B GGUF](https://huggingface.co/TheBloke/BioMistral-7B-GGUF) using `llama-cpp-python`.
+
+## 💡 Features
+
+- Runs locally in your browser using Streamlit
+- Doesn't require OpenAI or API keys
+- Runs fully offline once model is downloaded
+
+## 🧠 Model: BioMistral-7B.Q4_K_M.gguf
+
+Deployed using [llama.cpp](https://github.com/ggerganov/llama.cpp) backend.
+
+## 🚀 Usage
+
+- Click `App` tab to start chatting with the doctor.

app.py

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+import streamlit as st
+from llama_cpp import Llama
+
+st.set_page_config(page_title="🩺 Medical Chatbot", layout="centered")
+st.title("🩺 Medical Chatbot (Doctor Mistral)")
+
+@st.cache_resource
+def load_model():
+    return Llama(
+        model_path="./models/BioMistral-7B.Q4_K_M.gguf",  # <- Update to your actual path
+        n_ctx=2048,
+        n_threads=8,
+        n_gpu_layers=0  # Use GPU if available
+    )
+
+llm = load_model()
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+prompt = st.text_input("Ask me a medical question:")
+
+if st.button("Send") and prompt:
+    st.session_state.chat_history.append(("You", prompt))
+    full_prompt = f"You are a helpful medical assistant.\n\nUser: {prompt}\nDoctor:"
+    output = llm(full_prompt, max_tokens=512, stop=["User:", "Doctor:"])
+    reply = output["choices"][0]["text"].strip()
+    st.session_state.chat_history.append(("Doctor", reply))
+
+# Show chat history
+for role, message in st.session_state.chat_history:
+    st.markdown(f"**{role}:** {message}")

requirements.txt

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+streamlit>=1.30.0
+llama-cpp-python==0.2.72