Create app1.py

app1.py

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+# app.py
+
+import os
+import streamlit as st
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langgraph.graph import StateGraph, END
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_core.messages import AIMessage, HumanMessage
+from typing import Annotated
+from typing_extensions import TypedDict
+from langchain_together import Together
+from tools import execute_python_code
+import io
+import contextlib
+import traceback
+import time
+
+# Load environment variables
+load_dotenv()
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+together_api_key = os.getenv("TOGETHER_API_KEY")
+
+# LangGraph State definition
+class State(TypedDict):
+    messages: Annotated[list, ...]
+    name: str
+    birthday: str
+    input: str
+    code: str
+    explanation: str
+    execution_result: str
+
+# LLM
+code_generator = Together(
+    model="deepseek-ai/DeepSeek-R1-Distill-Llama-70B-free",
+    temperature=0.2,
+    max_tokens=1500,
+    api_key=together_api_key,
+)
+
+# Memory
+memory = MemorySaver()
+
+# Define LangGraph Nodes
+def generate_code(state: State):
+    user_prompt = state["input"]
+    system_prompt = """You are an expert Python coding assistant specializing in LangGraph applications.
+Generate clean, working Python code for the user's request with these requirements:
+1. The code MUST use the LangGraph framework (langgraph library).
+2. Implement a proper flow graph using StateGraph.
+3. Include all necessary imports and make sure the code is complete.
+4. Include code to visualize the flow graph.
+5. Output ONLY the final Python code.
+
+User request:"""
+    full_prompt = system_prompt + user_prompt
+
+    for attempt in range(3):
+        try:
+            response = code_generator.invoke(full_prompt)
+            return {**state, "code": str(response)}
+        except Exception as e:
+            if "503" in str(e):
+                time.sleep(2)
+            else:
+                raise e
+    raise Exception("Code generation failed after retries.")
+
+def execute_code(state: State):
+    code = state.get("code", "")
+    buffer = io.StringIO()
+    try:
+        with contextlib.redirect_stdout(buffer):
+            exec(code, {})
+        output = buffer.getvalue() or "✅ Code executed successfully with no output."
+    except Exception:
+        output = "❌ Execution Error:\n" + traceback.format_exc()
+    return {**state, "execution_result": output}
+
+def explain_code(state: State):
+    code = state["code"]
+    user_prompt = state["input"]
+    system_prompt = """You are a LangGraph expert who explains code clearly. Provide a detailed explanation of the code in three parts:
+1. LANGGRAPH FLOW: Describe nodes, edges, and how the graph flows.
+2. CODE FLOW: High-level architecture and logic.
+3. STEP-BY-STEP: Explain each part of the code so a beginner can understand it.
+4. VISUALIZATION: Instructions on how to run and see the graph output.
+"""
+
+    prompt = f"User Prompt: {user_prompt}\n\nCode:\n```python\n{code}\n```"
+    full_prompt = system_prompt + prompt
+
+    explanation = code_generator.invoke(full_prompt)
+    return {**state, "explanation": explanation}
+
+# LangGraph setup
+builder = StateGraph(State)
+builder.add_node("Generate_Code", generate_code)
+builder.add_node("Execute_Code", execute_code)
+builder.add_node("Code_Explainer", explain_code)
+builder.set_entry_point("Generate_Code")
+graph = builder.compile(checkpointer=memory)
+
+# Streamlit UI Setup
+st.set_page_config(page_title="MitraVerse", layout="wide")
+
+st.markdown("""
+<style>
+.stChatMessage {
+    padding: 12px;
+    margin-bottom: 12px;
+    border-radius: 12px;
+    max-width: 90%;
+}
+.user {
+    background-color: #dcf8c6;
+    align-self: flex-end;
+}
+.bot {
+    background-color: #f1f0f0;
+    align-self: flex-start;
+}
+.input-box {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+}
+#floating-container {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 0.25rem 0.75rem;
+    background-color: #f9f9f9;
+    border-radius: 0.75rem;
+    margin-top: 1rem;
+    border: 1px solid #ccc;
+}
+.floating-popup {
+    margin-top: 0.5rem;
+    padding: 0.5rem;
+    border-radius: 0.5rem;
+    border: 1px solid #ccc;
+    background-color: white;
+}
+</style>
+""", unsafe_allow_html=True)
+
+st.title("🧠 MitraVerse")
+
+# Initialize session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+if "latest_code" not in st.session_state:
+    st.session_state.latest_code = ""
+if "latest_explanation" not in st.session_state:
+    st.session_state.latest_explanation = ""
+if "latest_input" not in st.session_state:
+    st.session_state.latest_input = ""
+
+# Display chat history
+for msg in st.session_state.chat_history:
+    role = "user" if isinstance(msg, HumanMessage) else "bot"
+    st.markdown(f"<div class='stChatMessage {role}'>{msg.content}</div>", unsafe_allow_html=True)
+
+# Input form
+with st.form("chat_form", clear_on_submit=True):
+    st.markdown('<div id="floating-container">', unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)
+    user_input = st.text_input("Ask me", label_visibility="collapsed", placeholder="Ask me Anything")
+    submitted = st.form_submit_button(label="Send")
+
+    if submitted and user_input:
+        st.session_state.chat_history.append(HumanMessage(content=user_input))
+        st.session_state.latest_input = user_input
+
+# Buttons to run each tool manually
+if st.session_state.latest_input:
+    if st.button("🔨 Generate Code"):
+        state_input = {
+            "messages": st.session_state.chat_history,
+            "input": st.session_state.latest_input
+        }
+        result = graph.invoke(state_input, node="Generate_Code")
+        st.session_state.latest_code = result["code"]
+        st.session_state.chat_history.append(
+            AIMessage(content="**💻 Generated Code:**\n\n```python\n" + result["code"] + "\n```")
+        )
+        st.code(result["code"], language="python")
+
+    if st.button("⚙️ Execute Code") and st.session_state.latest_code:
+        state_input = {
+            "code": st.session_state.latest_code,
+            "input": st.session_state.latest_input
+        }
+        result = graph.invoke(state_input, node="Execute_Code")
+        st.session_state.chat_history.append(
+            AIMessage(content="**🧪 Execution Result:**\n\n" + result["execution_result"])
+        )
+        st.text("🧪 Execution Result:")
+        st.text(result["execution_result"])
+
+    if st.button("🧠 Explain Code") and st.session_state.latest_code:
+        state_input = {
+            "code": st.session_state.latest_code,
+            "input": st.session_state.latest_input
+        }
+        result = graph.invoke(state_input, node="Code_Explainer")
+        st.session_state.latest_explanation = result["explanation"]
+        st.session_state.chat_history.append(
+            AIMessage(content="**🔍 Code Explanation:**\n\n" + result["explanation"])
+        )
+        with st.expander("🔍 Code Explanation"):
+            st.markdown(result["explanation"])