Create app.py

app.py

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+import os
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.messages import AIMessage
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+import gradio as gr
+
+
+# Set your API keys from environment variables
+langchain_key = os.getenv("LANGCHAIN_API_KEY")
+HF_key = os.getenv("HUGGINGFACEHUB_TOKEN")
+LANGCHAIN_TRACING_V2=True
+LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
+LANGCHAIN_PROJECT="LLM_CHATBOT"
+
+os.environ["LANGCHAIN_TRACING_V2"] = str(LANGCHAIN_TRACING_V2)
+os.environ["LANGCHAIN_API_KEY"] = langchain_key
+os.environ["HUGGINGFACEHUB_TOKEN"] = HF_key
+os.environ["LANGCHAIN_ENDPOINT"] = LANGCHAIN_ENDPOINT
+os.environ["LANGCHAIN_PROJECT"] = LANGCHAIN_PROJECT
+
+# Initialize the Chat Model
+llm = HuggingFaceEndpoint(
+    repo_id="microsoft/Phi-3-vision-128k-instruct",
+    task="text-generation",
+    max_new_tokens=150,
+    do_sample=False,
+    token =HF_key
+)
+
+# Create a Chat Prompt Template
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful assistant. Answer all questions to the best of your ability."),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+# Set up the chain
+chain = prompt | llm
+
+# Set up message history
+store = {}
+
+def get_session_history(session_id: str) -> BaseChatMessageHistory:
+    if session_id not in store:
+        store[session_id] = ChatMessageHistory()
+    return store[session_id]
+
+with_message_history = RunnableWithMessageHistory(chain, get_session_history)
+
+# Gradio chat function
+def chat(session_id, user_input):
+    config = {"configurable": {"session_id": session_id}}
+    human_message = HumanMessage(content=user_input)
+    response = with_message_history.invoke({"messages": [human_message]}, config=config)
+    return response
+
+# Gradio interface
+iface = gr.Interface(
+    fn=chat,
+    inputs=[gr.Textbox(lines=1, placeholder="Enter Session ID"), gr.Textbox(lines=7, placeholder="Enter your message")],
+    outputs="text",
+    title="LangChain Chatbot",
+    description="A chatbot that remembers your past interactions. Enter your session ID and message."
+)
+
+# Launch the app
+iface.launch()
+