Create app.py

app.py

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+import os
+from typing import Annotated
+from typing_extensions import TypedDict
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langchain_huggingface import HuggingFaceEndpoint
+from dotenv import load_dotenv
+import logging
+import gradio as gr
+
+# Initialize logging
+logging.basicConfig(level=logging.INFO)
+
+# Load environment variables
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Initialize Hugging Face endpoint
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    huggingfacehub_api_token=HF_TOKEN.strip(),
+    temperature=0.7,
+    max_new_tokens=200
+)
+
+# Define the state structure
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+# Create a state graph builder
+graph_builder = StateGraph(State)
+
+# Define the chatbot function
+def chatbot(state: State):
+    try:
+        logging.info(f"Input Messages: {state['messages']}")
+        response = llm.invoke(state["messages"])
+        logging.info(f"LLM Response: {response}")
+        return {"messages": [response]}
+    except Exception as e:
+        logging.error(f"Error: {str(e)}")
+        return {"messages": [f"Error: {str(e)}"]}
+
+# Add nodes and edges to the state graph
+graph_builder.add_node("chatbot", chatbot)
+graph_builder.add_edge(START, "chatbot")
+graph_builder.add_edge("chatbot", END)
+
+# Compile the state graph
+graph = graph_builder.compile()
+
+# Function to stream updates from the graph
+def stream_graph_updates(user_input: str):
+    """
+    Stream updates from the graph based on user input and return the assistant's reply.
+    """
+    assistant_reply = ""
+    for event in graph.stream({"messages": [("user", user_input)]}):
+        for value in event.values():
+            assistant_reply = value["messages"][-1].content
+    return assistant_reply
+
+# Gradio chatbot function using the streaming updates
+def gradio_chatbot(user_message: str):
+    """
+    Handle Gradio user input, process through the graph, and return only the assistant's reply.
+    """
+    try:
+        return stream_graph_updates(user_message)
+    except Exception as e:
+        logging.error(f"Error in Gradio chatbot: {str(e)}")
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=gradio_chatbot,
+    inputs=gr.Textbox(placeholder="Enter your message", label="Your Message"),
+    outputs=gr.Textbox(label="Assistant's Reply"),
+    title="Chatbot",
+    description="Interactive chatbot using a state graph and Hugging Face Endpoint."
+)
+
+if __name__ == "__main__":
+    interface.launch()