Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,85 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+"""Streamlit app for chatting with Hugging Face model using streaming inference."""
+
+import os
 import streamlit as st
+from huggingface_hub import InferenceClient
+
+# Get token from environment variable set via Hugging Face Secrets
+HF_TOKEN = os.environ["HF_TOKEN"]  # Raises KeyError if not set
+
+# Initialize Hugging Face Inference Client
+client = InferenceClient(
+    provider="novita",
+    api_key=HF_TOKEN,
+)
+
+
+def init_session():
+    """Initialize session state variables for chat history."""
+    if "messages" not in st.session_state:
+        st.session_state.messages = [
+            {"role": "system", "content": "You are a helpful assistant."}
+        ]
+    if "user_input" not in st.session_state:
+        st.session_state.user_input = ""
+
+
+def display_chat():
+    """Render chat messages on the Streamlit app."""
+    for msg in st.session_state.messages:
+        if msg["role"] == "user":
+            with st.chat_message("user"):
+                st.markdown(msg["content"])
+        elif msg["role"] == "assistant":
+            with st.chat_message("assistant"):
+                st.markdown(msg["content"])
+
+
+def stream_response(model_name: str, messages: list[dict]) -> str:
+    """
+    Stream the model's response from Hugging Face InferenceClient.
+
+    Args:
+        model_name (str): The name of the model to use.
+        messages (list): List of messages in the conversation.
+
+    Returns:
+        str: Full response streamed from the model.
+    """
+    response = ""
+    with st.chat_message("assistant"):
+        msg_placeholder = st.empty()
+        for chunk in client.chat.completions.create(
+            model=model_name,
+            messages=messages,
+            stream=True,
+        ):
+            if chunk.choices[0].delta.content:
+                response += chunk.choices[0].delta.content
+                msg_placeholder.markdown(response)
+    return response
+
+
+def main():
+    """Main function for Streamlit app."""
+    st.set_page_config(page_title="ERNIE Chat", page_icon="🧠", layout="centered")
+    st.title("💬 ERNIE Chat - Hugging Face")
+
+    init_session()
+    display_chat()
+
+    user_prompt = st.chat_input("Say something...")
+    if user_prompt:
+        # Append user message to session state
+        st.session_state.messages.append({"role": "user", "content": user_prompt})
+
+        # Stream and collect assistant response
+        model_name = "baidu/ERNIE-4.5-0.3B-PT"
+        assistant_reply = stream_response(model_name, st.session_state.messages)
+
+        # Append assistant message to session state
+        st.session_state.messages.append({"role": "assistant", "content": assistant_reply})
+
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()