Initial Commit

app.py

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+import gradio as gr
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import trim_messages
+from langgraph.graph import START, MessagesState, StateGraph
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+
+
+
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.2-3B-Instruct",
+    max_new_tokens=512,
+    temperature=0.7,
+)
+model = ChatHuggingFace(llm=llm)
+
+# Define message trimmer
+trimmer = trim_messages(
+    max_tokens=8192,
+    strategy="last",
+    token_counter=model,
+    include_system=True,
+    allow_partial=False,
+    start_on="human",
+)
+
+
+# Define the workflow
+workflow = StateGraph(state_schema=MessagesState)
+app = workflow.compile(checkpointer=MemorySaver())
+
+config = {"configurable": {"thread_id": "abc123"}}
+
+
+# Function to handle chat interaction
+def chat_fn(system_prompt, user_input, history):
+    if history is None:
+        history = []
+
+    # Append user input
+    history.append((user_input, ""))
+
+    # Build messages for the model
+    messages = [SystemMessage(system_prompt)]
+    for user_msg, assistant_msg in history[:-1]:
+        messages.append(HumanMessage(user_msg))
+        messages.append(AIMessage(assistant_msg))
+    messages.append(HumanMessage(user_input))
+
+    # Trim messages
+    trimmed_messages = trimmer.invoke(messages)
+
+    # Create prompt template with current system prompt
+    prompt_template = ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            MessagesPlaceholder(variable_name="messages"),
+        ]
+    )
+
+    # Prepare the prompt
+    prompt = prompt_template.invoke(trimmed_messages)
+
+    # Call the model
+    response = model.invoke(prompt)
+
+    # Get assistant's reply
+    print(response)
+    # assistant_reply = response["messages"][-1].content
+    assistant_reply = response.content
+
+    # Update history with assistant's reply
+    history[-1] = (user_input, assistant_reply)
+
+    return history, history
+
+
+# Build Gradio interface
+with gr.Blocks() as demo:
+    system_prompt = gr.Textbox(
+        value="You talk like a pirate. Answer all questions to the best of your ability.",
+        label="System Prompt",
+        lines=2,
+    )
+    chatbot = gr.Chatbot()
+    state = gr.State()
+
+    with gr.Row():
+        user_input = gr.Textbox(
+            show_label=False,
+            placeholder="Enter your message",
+            container=False,  # Moved 'container' parameter here
+        )
+        send_button = gr.Button("Send")
+
+    # Define interaction
+    def user_message(_):
+        return "", ""
+
+    send_button.click(
+        fn=chat_fn,
+        inputs=[system_prompt, user_input, state],
+        outputs=[chatbot, state],
+    )
+    user_input.submit(
+        fn=chat_fn,
+        inputs=[system_prompt, user_input, state],
+        outputs=[chatbot, state],
+    )
+
+if __name__ == '__main__':
+    demo.launch()

requirements.txt

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+gradio
+langchain_core
+langgraph
+langchain_core
+langchain_huggingface