Add app and requirements

app.py

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+# Following https://python.langchain.com/docs/tutorials/chatbot/
+# Missing: trimming, streaming with memory, use multiple threads
+
+from langchain_mistralai import ChatMistralAI
+from langchain_core.rate_limiters import InMemoryRateLimiter
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, MessagesState, StateGraph
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage, AIMessage
+import gradio as gr
+
+# Prompt template
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are an AI assistant. Answer all questions to the best of your ability.",
+        ),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+# Rate limiter
+rate_limiter = InMemoryRateLimiter(
+    requests_per_second=0.1,  # <-- MistralAI free. We can only make a request once every second
+    check_every_n_seconds=0.01,  # Wake up every 100 ms to check whether allowed to make a request,
+    max_bucket_size=10,  # Controls the maximum burst size.
+)
+
+model = ChatMistralAI(model="mistral-large-latest", rate_limiter=rate_limiter)
+
+# Define a new graph
+workflow = StateGraph(state_schema=MessagesState)
+
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    chain = prompt | model
+    response = chain.invoke(state)
+    return {"messages": response}
+
+# Define the (single) node in the graph
+workflow.add_edge(START, "model")
+workflow.add_node("model", call_model)
+
+# Add memory
+memory = MemorySaver()
+app = workflow.compile(checkpointer=memory)
+
+# Config with thread
+config = {"configurable": {"thread_id": "abc345"}}
+
+
+def handle_prompt(query, history):
+    input_messages = [HumanMessage(query)]
+    try:
+        # Stream output
+        # out=""
+        # for chunk, metadata in app.stream({
+        #                         "messages": input_messages},
+        #                         config,
+        #                         stream_mode="messages"):
+        #     if isinstance(chunk, AIMessage):  # Filter to just model responses
+        #         out += chunk.content
+        #     yield out
+        output = app.invoke({"messages": input_messages}, config)
+        return output["messages"][-1].content
+    except:
+        raise gr.Error("Requests rate limit exceeded")
+
+description = "A MistralAI powered chatbot, using Langchain and deployed with Gradio."
+
+demo = gr.ChatInterface(handle_prompt, type="messages", title="Medieval ChatBot", theme=gr.themes.Citrus(), description=description)
+
+demo.launch()

requirements.txt

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+langchain-core
+langgraph>0.2.27
+langchain_mistralai
+gradio