add simple chat app

Author: ks6088ts

.env.template

@@ -27,7 +27,7 @@ AZURE_AI_FOUNDRY_INFERENCE_API_VERSION="2025-04-01-preview"
 AZURE_AI_FOUNDRY_INFERENCE_MODEL_CHAT="gpt-5"
 
 ## Ollama Settings
-OLLAMA_MODEL_CHAT="gemma3:270m"
+OLLAMA_MODEL_CHAT="gemma3:latest"
 
 # ---------
 # Tools

template_langgraph/services/streamlits/pages/chat_langchain.py

@@ -0,0 +1,191 @@
+from base64 import b64encode
+from os import getenv
+
+import streamlit as st
+from dotenv import load_dotenv
+from langchain_community.callbacks.streamlit import (
+    StreamlitCallbackHandler,
+)
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_ollama import ChatOllama
+from langchain_openai import AzureChatOpenAI
+from openai import APIConnectionError, APIStatusError, APITimeoutError
+
+from template_langgraph.loggers import get_logger
+
+load_dotenv(override=True)
+logger = get_logger(__name__)
+logger.setLevel("DEBUG")
+
+
+def image_to_base64(image_bytes: bytes) -> str:
+    """Convert image bytes to base64 string."""
+    return b64encode(image_bytes).decode("utf-8")
+
+
+with st.sidebar:
+    "# Common Settings"
+    stream_mode = st.checkbox(
+        label="ストリーム出力を有効にする",
+        value=True,
+        key="STREAM_MODE",
+    )
+    "# Model"
+    model_choice = st.radio(
+        label="Active Model",
+        options=["azure", "ollama"],
+        index=0,
+        key="model_choice",
+    )
+    "## Model Settings"
+    if model_choice == "azure":
+        azure_openai_endpoint = st.text_input(
+            label="AZURE_OPENAI_ENDPOINT",
+            value=getenv("AZURE_OPENAI_ENDPOINT"),
+            key="AZURE_OPENAI_ENDPOINT",
+            type="default",
+        )
+        azure_openai_api_key = st.text_input(
+            label="AZURE_OPENAI_API_KEY",
+            value=getenv("AZURE_OPENAI_API_KEY"),
+            key="AZURE_OPENAI_API_KEY",
+            type="password",
+        )
+        azure_openai_api_version = st.text_input(
+            label="AZURE_OPENAI_API_VERSION",
+            value=getenv("AZURE_OPENAI_API_VERSION"),
+            key="AZURE_OPENAI_API_VERSION",
+            type="default",
+        )
+        azure_openai_model_chat = st.text_input(
+            label="AZURE_OPENAI_MODEL_CHAT",
+            value=getenv("AZURE_OPENAI_MODEL_CHAT"),
+            key="AZURE_OPENAI_MODEL_CHAT",
+            type="default",
+        )
+        "[Azure Portal](https://portal.azure.com/)"
+        "[Azure OpenAI Studio](https://oai.azure.com/resource/overview)"
+        "[View the source code](https://github.com/ks6088ts-labs/template-streamlit)"
+    else:
+        ollama_model_chat = st.text_input(
+            label="OLLAMA_MODEL_CHAT",
+            value=getenv("OLLAMA_MODEL_CHAT"),
+            key="OLLAMA_MODEL_CHAT",
+            type="default",
+        )
+        "[Ollama Docs](https://github.com/ollama/ollama)"
+        "[View the source code](https://github.com/ks6088ts-labs/template-streamlit)"
+
+
+def is_azure_configured():
+    return (
+        st.session_state.get("AZURE_OPENAI_API_KEY")
+        and st.session_state.get("AZURE_OPENAI_ENDPOINT")
+        and st.session_state.get("AZURE_OPENAI_API_VERSION")
+        and st.session_state.get("AZURE_OPENAI_MODEL_CHAT")
+        and st.session_state.get("model_choice") == "azure"
+    )
+
+
+def is_ollama_configured():
+    return st.session_state.get("OLLAMA_MODEL_CHAT") and st.session_state.get("model_choice") == "ollama"
+
+
+def is_configured():
+    return is_azure_configured() or is_ollama_configured()
+
+
+def get_model():
+    if is_azure_configured():
+        return AzureChatOpenAI(
+            azure_endpoint=st.session_state.get("AZURE_OPENAI_ENDPOINT"),
+            api_key=st.session_state.get("AZURE_OPENAI_API_KEY"),
+            openai_api_version=st.session_state.get("AZURE_OPENAI_API_VERSION"),
+            azure_deployment=st.session_state.get("AZURE_OPENAI_MODEL_CHAT"),
+        )
+    elif is_ollama_configured():
+        return ChatOllama(
+            model=st.session_state.get("OLLAMA_MODEL_CHAT", ""),
+        )
+    raise ValueError("No model is configured. Please set up the Azure or Ollama model in the sidebar.")
+
+
+st.title("chat app with LangChain SDK")
+
+if not is_configured():
+    st.warning("Please fill in the required fields at the sidebar.")
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        AIMessage(content="Hello! I'm a helpful assistant."),
+    ]
+
+# Show chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message.type):
+        if isinstance(message.content, str):
+            st.markdown(message.content)
+        else:
+            for item in message.content:
+                if item["type"] == "text":
+                    st.markdown(item["text"])
+                elif item["type"] == "image_url":
+                    st.image(item["image_url"]["url"])
+
+
+# Receive user input
+uploaded_file = st.file_uploader("画像をアップロード", type=["png", "jpg", "jpeg"], key="file_uploader")
+if prompt := st.chat_input(disabled=not is_configured()):
+    user_message_content = [{"type": "text", "text": prompt}]
+    if uploaded_file:
+        image_bytes = uploaded_file.getvalue()
+        base64_image = image_to_base64(image_bytes)
+        image_url = f"data:image/jpeg;base64,{base64_image}"
+        user_message_content.append({"type": "image_url", "image_url": {"url": image_url}})
+
+    user_message = HumanMessage(content=user_message_content)
+    st.session_state.messages.append(user_message)
+
+    with st.chat_message("user"):
+        for item in user_message_content:
+            if item["type"] == "text":
+                st.markdown(item["text"])
+            elif item["type"] == "image_url":
+                st.image(item["image_url"]["url"])
+
+    with st.spinner("Thinking..."):
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            llm = get_model()
+            callbacks = [StreamlitCallbackHandler(st.container())]
+
+            try:
+                if stream_mode:
+                    for chunk in llm.stream(st.session_state.messages):
+                        if chunk.content is not None:
+                            full_response += chunk.content
+                            message_placeholder.markdown(full_response + "▌")
+                    message_placeholder.markdown(full_response)
+                else:
+                    response = llm.invoke(input=st.session_state.messages)
+                    full_response = response.content if hasattr(response, "content") else str(response)
+                    message_placeholder.markdown(full_response)
+
+                st.session_state.messages.append(AIMessage(content=full_response))
+
+            except APITimeoutError as e:
+                logger.exception(f"APIタイムアウトエラーが発生しました: {e}")
+                st.error(f"APIタイムアウトエラーが発生しました: {e}")
+                st.warning("再度お試しいただくか、接続を確認してください。")
+            except APIConnectionError as e:
+                logger.exception(f"API接続エラーが発生しました: {e}")
+                st.error(f"API接続エラーが発生しました: {e}")
+                st.warning("ネットワーク接続を確認してください。")
+            except APIStatusError as e:
+                logger.exception(f"APIステータスエラーが発生しました: {e.status_code} - {e.response}")
+                st.error(f"APIステータスエラーが発生しました: {e.status_code} - {e.response}")
+                st.warning("Azure OpenAIの設定（デプロイメント名、APIバージョンなど）を確認してください。")
+            except Exception as e:
+                logger.exception(f"予期せぬエラーが発生しました: {e}")
+                st.error(f"予期せぬエラーが発生しました: {e}")