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| import streamlit as st | |
| import os | |
| from dotenv import load_dotenv | |
| from agent.graph import app | |
| from agent.state import AgentState | |
| load_dotenv() | |
| st.set_page_config(page_title="AutoStream AI Sales Assistant", page_icon="🎬", layout="centered") | |
| # Custom CSS for minimalist, cooler UI | |
| st.markdown(""" | |
| <style> | |
| .stChatFloatingInputContainer { | |
| bottom: 20px; | |
| } | |
| .main { | |
| background-color: #0E1117; | |
| } | |
| h1 { | |
| color: #E2E8F0; | |
| font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; | |
| font-weight: 700; | |
| text-align: center; | |
| margin-bottom: 2rem; | |
| } | |
| .subtitle { | |
| color: #94A3B8; | |
| text-align: center; | |
| margin-bottom: 2rem; | |
| font-size: 1.1rem; | |
| } | |
| .stAlert { | |
| border-radius: 8px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| st.session_state.agent_state = AgentState( | |
| conversation_history=[], | |
| current_message="", | |
| detected_intent=None, | |
| retrieved_documents=[], | |
| user_name=None, | |
| user_email=None, | |
| creator_platform=None, | |
| lead_ready=False, | |
| response="" | |
| ) | |
| st.session_state.messages.append({"role": "assistant", "content": "Hello! I'm the AutoStream assistant. I can answer questions about our features and pricing. How can I help you today?"}) | |
| st.markdown("<h1>🎬 AutoStream Assistant</h1>", unsafe_allow_html=True) | |
| st.markdown("<div class='subtitle'>Ask about features and pricing, or sign up for a plan instantly!</div>", unsafe_allow_html=True) | |
| if not os.environ.get("OPENAI_API_KEY"): | |
| st.info("ℹ️ OPENAI_API_KEY is not set. The system will fall back to a local Qwen model and HuggingFace embeddings.") | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| if prompt := st.chat_input("What would you like to know?"): | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| st.session_state.agent_state["current_message"] = prompt | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| try: | |
| result_state = app.invoke(st.session_state.agent_state) | |
| st.session_state.agent_state = result_state | |
| response = result_state["response"] | |
| st.session_state.agent_state["conversation_history"].append({"role": "user", "content": prompt}) | |
| st.session_state.agent_state["conversation_history"].append({"role": "assistant", "content": response}) | |
| if len(st.session_state.agent_state["conversation_history"]) > 12: | |
| st.session_state.agent_state["conversation_history"] = st.session_state.agent_state["conversation_history"][-12:] | |
| st.markdown(response) | |
| with st.expander("Agent Reasoning & State", expanded=False): | |
| st.write(f"**Detected Intent:** `{result_state.get('detected_intent', 'UNKNOWN')}`") | |
| if result_state.get("retrieved_documents") and result_state.get("detected_intent") in ["PRODUCT_QUERY", "PRICING_QUERY"]: | |
| st.write(f"**RAG Retrieval:** Found {len(result_state['retrieved_documents'])} relevant knowledge chunks.") | |
| st.write("**Lead Data:**") | |
| st.json({ | |
| "user_name": result_state.get("user_name"), | |
| "user_email": result_state.get("user_email"), | |
| "creator_platform": result_state.get("creator_platform"), | |
| "lead_ready": result_state.get("lead_ready") | |
| }) | |
| except Exception as e: | |
| response = f"An error occurred: {str(e)}" | |
| st.error(response) | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |