Create new_app.py

new_app.py

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+from langgraph.graph import StateGraph, END, START
+from langgraph.checkpoint.memory import MemorySaver
+from typing_extensions import TypedDict
+from typing import Annotated, Dict, Any, List
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.runnables import Runnable
+from langchain.chat_models import ChatOpenAI  # Or ChatGroq, ChatTogether
+
+# -------------------
+# Define State
+# -------------------
+class State(TypedDict):
+    messages: Annotated[List, lambda x: isinstance(x, list)]
+    answers: Annotated[List, lambda x: isinstance(x, list)]
+    retry_count: int
+    questions: Annotated[List, lambda x: isinstance(x, list)]  # added
+    code: str
+    explanation: str
+    task_plan: str
+    user_input: str
+
+
+# -------------------
+# Initialize LLM
+# -------------------
+# llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)  # Replace with ChatGroq or Together if needed
+question_model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.7)
+
+llm_agent_model = ChatTogether(model="mistralai/Mistral-7B-Instruct-v0.1")
+
+code_model = ChatTogether(model="deepseek-ai/deepseek-coder-6.7b-instruct")
+
+confirm_model = ChatGroq(model="qwen/qwen3-32b")
+
+explain_model = ChatGroq(model="meta-llama/llama-guard-4-12b")
+# -------------------
+# Define Node Functions
+# -------------------
+def llm_agent(state: State) -> State:
+    messages = [
+        SystemMessage(content="You are an AI task planner. Break down user instructions."),
+        HumanMessage(content=state["user_input"])
+    ]
+    response = llm_agent_model.invoke(messages)
+    state["task_plan"] = response.content
+    return state
+
+
+def generate_questions(state: State) -> State:
+    messages = [
+        SystemMessage(content="You generate follow-up questions to clarify vague instructions."),
+        HumanMessage(content=state["answers"][0])
+    ]
+    response = question_model.invoke(messages)
+    state["questions"] = response.content
+    return state
+
+
+def generate_code(state: State) -> State:
+    messages = [
+        SystemMessage(content="You are a coding expert. Generate clean, well-documented Python code."),
+        HumanMessage(content=state["answers"][0])
+    ]
+    response = code_model.invoke(messages)
+    state["code"] = response.content
+    return state
+
+def handle_answers(state: State) -> State:
+    print("Handling answers...")
+
+    answer = state["answers"][0]
+    system_prompt = "You are a helpful assistant that confirms the received idea."
+    user_msg = f"The user said: '{answer}'. Confirm and move ahead."
+
+    response = confirm_model.invoke([
+        SystemMessage(content=system_prompt),
+        HumanMessage(content=user_msg)
+    ])
+
+    state["messages"].append(response.content.strip())
+    return state
+
+def explain_code(state: State) -> State:
+    print("Explaining code...")
+
+    code = state["code"]
+    system_prompt = "You are a Python tutor. Explain what the following code does in simple terms."
+    user_msg = f"Code:\n{code}"
+
+    response = explain_model.invoke([
+        SystemMessage(content=system_prompt),
+        HumanMessage(content=user_msg)
+    ])
+
+    state["explanation"] = response.content.strip()
+    return state
+    
+def wait_for_answers(state: State) -> State:
+    print("Waiting for answers...")
+
+    state["retry_count"] = state.get("retry_count", 0) + 1
+
+    # Simulate receiving an answer after 2 retries
+    if state["retry_count"] >= 2:
+        state["answers"] = ["Build a calculator app"]
+    return state
+
+
+# -------------------
+# Define Condition Function
+# -------------------
+
+MAX_RETRIES = 3
+
+def check_if_answered(state: State) -> str:
+    if "answers" in state and state["answers"]:
+        return "answered"
+    elif state.get("retry_count", 0) >= MAX_RETRIES:
+        print("Max retries reached. Proceeding anyway.")
+        return "answered"
+    else:
+        return "not_answered"
+
+
+# -------------------
+# Build the Graph
+# -------------------
+
+builder = StateGraph(State)
+
+builder.add_node("LLM_Agent", llm_agent)
+builder.add_node("Generate_Questions", generate_questions)
+builder.add_node("Wait_For_Answers", wait_for_answers)
+builder.add_node("Handle_Answers", handle_answers)
+builder.add_node("Generate_Code", generate_code)
+builder.add_node("Code_Explainer", explain_code)
+
+builder.set_entry_point("LLM_Agent")
+
+builder.add_edge("LLM_Agent", "Generate_Questions")
+builder.add_conditional_edges(
+    "Generate_Questions",
+    check_if_answered,
+    {
+        "answered": "Handle_Answers",
+        "not_answered": "Wait_For_Answers"
+    }
+)
+builder.add_edge("Wait_For_Answers", "Generate_Questions")
+builder.add_edge("Handle_Answers", "Generate_Code")
+builder.add_edge("Generate_Code", "Code_Explainer")
+builder.add_edge("Code_Explainer", END)
+
+# -------------------
+# Compile and Run
+# -------------------
+
+memory = MemorySaver()
+graph = builder.compile(checkpointer=memory)
+
+inputs = {"messages": [], "answers": [], "retry_count": 0, "code": "", "explanation": "", "questions": [], "task_plan" :"","user_input": "I want to create an agent"}
+
+for step in graph.stream(inputs):
+    for key, val in step.items():
+        print(f"\n--- {key} ---\n{val}")