streamlit push

app.py

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+import streamlit as st
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, AIMessage, trim_messages
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, MessagesState, StateGraph
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from dotenv import load_dotenv
+load_dotenv()
+
+@st.cache_resource
+def get_model():
+    model = ChatOpenAI(model="gpt-4o", temperature=0, base_url="https://models.inference.ai.azure.com")
+    return model
+
+model = get_model()
+
+
+system_prompt = """You are an intelligent and versatile AI assistant, capable of engaging in natural, helpful, and coherent conversations. Your primary role is to assist users with a wide range of topics, including answering questions, providing recommendations, solving problems, generating creative content, and offering technical guidance.
+
+Key Guidelines:
+
+1. Clarity and Precision: Provide clear, concise, and accurate responses. Tailor your tone and style to match the user’s needs and preferences.
+
+2. Helpfulness: Strive to be as useful as possible. Clarify ambiguous queries and ask for more details when needed.
+
+3. Adaptability: Adjust your responses based on the context and complexity of the user's request, from casual to professional interactions.
+
+4. Ethical and Safe: Ensure your responses are ethical, unbiased, and do not promote harm, misinformation, or illegal activities.
+
+5. Context Awareness: Leverage the context of the conversation to provide relevant and coherent replies, maintaining continuity throughout.
+
+6. Creative Problem-Solving: When asked for creative or technical solutions, provide innovative, practical, and actionable ideas.
+
+7. Limitations: Be transparent about your capabilities and limitations. If you cannot answer a question or perform a task, communicate this clearly and, when possible, suggest alternative resources.
+"""
+
+
+prompt_template = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            system_prompt,
+        ),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+trimmer = trim_messages(
+    max_tokens=65,
+    strategy="last",
+    token_counter=model,
+    include_system=True,
+    allow_partial=False,
+    start_on="human",
+)
+
+
+def call_model(state: MessagesState):
+    trimmed_messages = trimmer.invoke(state["messages"])
+    prompt = prompt_template.invoke({"messages" : trimmed_messages})
+    response = model.invoke(prompt)
+    return {"messages": response}
+
+
+@st.cache_resource
+def get_app(state=MessagesState):
+
+    workflow = StateGraph(state_schema=MessagesState)
+    workflow.add_edge(START, "model")
+    workflow.add_node("model", call_model)
+
+    memory = MemorySaver()
+    app = workflow.compile(checkpointer=memory)
+    return app
+
+config = {"configurable": {"thread_id": "111"}}
+app = get_app()
+
+if query:=st.chat_input("Ask anything"):
+    msg= [HumanMessage(query)]
+    def gen():
+        for chunk, metadata in app.stream({"messages": msg}, config=config, stream_mode="messages"):
+            if isinstance(chunk, AIMessage):
+                yield chunk.content
+    st.write_stream(gen)
+
+
+
+
+    

requirements.txt

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+langchain_community 
+langchain-openai 
+langchain 
+langgraph 
+langchainhub 
+streamlit
+
+
+