Code Editor for app.py

import streamlit as st
import requests
import time
import json # Import the json module to handle potential parsing errors

# --- Configuration ---
# The URL of your AI agent's backend
BACKEND_URL = "https://emea.snaplogic.com/api/1/rest/slsched/feed/ConnectFasterInc/snapLogic4snapLogic/Bootcamp_EMEA_August_2025/AgentDriver_Swati_Task"
# The bearer token for authentication
TOKEN = "Bearer 5555"

# --- Streamlit App UI ---
st.set_page_config(page_title="SnapLogic Best Practices", layout="centered")
st.title("🤖 SnapLogic Best Practices")
st.markdown("Get detailed answers to RFP questions and technical inquiries, with information sourced from official documentation of SnapLogic.")

# Add a section for sample queries
st.markdown("""
**Sample queries:**

- How to design the pipeline?
- What are the SnapLogic Tasks?
- Please recommend Pipeline Naming Conventions
- What are the best practices to design the pipeline?
- Best practices for the Pipeline Execute Snap
""")

# --- Initialize Chat History in Session State ---
# This ensures that the chat history persists across reruns
if "messages" not in st.session_state:
    st.session_state.messages = [
        {"role": "assistant", "content": "Hello! How can I help you today?"}
    ]

# --- Display Existing Chat Messages ---
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# --- Handle User Input ---
# This block is executed whenever the user types a message and hits enter
if prompt := st.chat_input("Type your message..."):
    # Add the user's message to the chat history and display it
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    # --- API Call to Backend ---
    try:
        # Display a "thinking" message from the assistant
        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                # Define the headers for the API request, including the Authorization token
                headers = {
                    "Authorization": TOKEN,
                    "Content-Type": "application/json"
                }

                # Send the user's prompt to the backend
                # Increased the timeout to 120 seconds to give the backend more time to respond.
                response = requests.post(
                    BACKEND_URL,
                    json={"prompt": prompt},
                    headers=headers, # Pass the headers with the token
                    timeout=120  # Increased timeout to 120 seconds
                )

                # Raise an exception for bad status codes
                response.raise_for_status()

                # Get the JSON response
                response_data = response.json()

                # Check if the response is a list and contains a dictionary
                if isinstance(response_data, list) and response_data and isinstance(response_data[0], dict):
                    # Safely get the 'response' key from the first item in the list
                    ai_response = response_data[0].get("response", "No response found.")
                elif isinstance(response_data, dict):
                    # If it's a dictionary, get the 'response' key
                    ai_response = response_data.get("response", "No response found.")
                else:
                    # For any other data type, convert it to a string
                    ai_response = str(response_data)


                st.session_state.messages.append({"role": "assistant", "content": ai_response})
                st.markdown(ai_response)

    except requests.exceptions.RequestException as e:
        # Handle connection errors, timeouts, and bad status codes
        error_message = f"Error: Failed to connect to the backend. Please check the URL and your connection. Details: {e}"
        st.session_state.messages.append({"role": "assistant", "content": error_message})
        with st.chat_message("assistant"):
            st.error(error_message)
    except json.JSONDecodeError as e:
        # Handle cases where the response is not valid JSON
        error_message = f"Error: Could not decode JSON response from the backend. Details: {e}"
        st.session_state.messages.append({"role": "assistant", "content": error_message})
        with st.chat_message("assistant"):
            st.error(error_message)

    # Rerun the app to display the new messages
    st.rerun()