General health chatbot

app.py

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+
+import streamlit as st
+from huggingface_hub import InferenceClient
+
+st.title("🩺 Health Assistant AI")
+st.caption("A helpful medical information chatbot")
+
+HF_TOKEN = st.secrets["HF_TOKEN"]
+client = InferenceClient(api_key=HF_TOKEN)
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "system", "content": "You are a helpful medical assistant. Always include a disclaimer."}]
+
+for message in st.session_state.messages[1:]:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("How can I help you today?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        response = client.chat.completions.create(
+            model="Qwen/Qwen2.5-72B-Instruct",
+            messages=st.session_state.messages,
+            max_tokens=500
+        )
+        full_response = response.choices[0].message.content
+        st.markdown(full_response)
+        st.session_state.messages.append({"role": "assistant", "content": full_response})

requirements.txt

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+streamlit
+huggingface-hub