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Update streamlit_app.py
Browse files- streamlit_app.py +28 -36
streamlit_app.py
CHANGED
@@ -1,17 +1,16 @@
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import streamlit as st
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import os
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import re
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from src.tools.accent_tool import AccentAnalyzerTool
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from src.app.main_agent import create_agent
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from langchain_core.messages import HumanMessage, AIMessage
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st.set_page_config(page_title="Accent Analyzer Agent", page_icon="💬", layout="centered")
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st.warning("⚠️ High latency due to CPU usage. Once migrated to GPU, response time will improve significantly.")
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st.title("English Accent Analyzer (Conversational)")
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st.subheader("Ask me to analyze a video URL, e.g.: \n\n
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# Load tool and agent
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@st.cache_resource
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def load_tool_and_agent():
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with st.spinner("Loading AI models and tools... This might take a moment."):
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@@ -23,17 +22,9 @@ def load_tool_and_agent():
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accent_tool_obj, analysis_agent, follow_up_agent = load_tool_and_agent()
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# Session state initialization
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = []
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if "pending_user_input" not in st.session_state:
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st.session_state.pending_user_input = None
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if "pending_spinner_msg" not in st.session_state:
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st.session_state.pending_spinner_msg = ""
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# Prompt configuration
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if hasattr(accent_tool_obj, "last_transcript") and accent_tool_obj.last_transcript:
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prompt_label = "Ask more about the video..."
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input_key = "followup"
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prompt_label = "Paste your prompt here..."
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input_key = "initial"
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# Chat input
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user_input = st.chat_input(prompt_label, key=input_key)
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#
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if user_input:
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st.session_state.chat_history.append(HumanMessage(content=user_input))
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if re.search(r'https?://\S+', user_input):
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accent_tool_obj.last_transcript = ""
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else:
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# Display chat history
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for msg in st.session_state.chat_history:
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with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
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st.markdown(msg.content)
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#
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if
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try:
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if re.search(r'https?://\S+', st.session_state.pending_user_input):
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response = analysis_agent.invoke(st.session_state.chat_history)[-1].content
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else:
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response = follow_up_agent.invoke(st.session_state.chat_history).content
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except Exception as e:
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response = f"Error: {str(e)}"
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st.session_state.chat_history.append(AIMessage(content=response))
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# Clear pending flags
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st.session_state.pending_user_input = None
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st.session_state.pending_spinner_msg = ""
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st.rerun()
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import streamlit as st
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from src.tools.accent_tool import AccentAnalyzerTool
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from src.app.main_agent import create_agent
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from langchain_core.messages import HumanMessage, AIMessage
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import re
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st.set_page_config(page_title="Accent Analyzer Agent", page_icon="💬", layout="centered")
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st.warning("⚠️ High latency due to CPU usage. Once migrated to GPU, response time will improve significantly.")
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st.title("English Accent Analyzer (Conversational)")
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st.subheader("Ask me to analyze a video URL, e.g.: \n\n Analyze this video: https://github.com/ash-171/Data-mp4/raw/refs/heads/main/NWRNVTFlRGlnV0FfNDgwcA_out.mp4")
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@st.cache_resource
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def load_tool_and_agent():
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with st.spinner("Loading AI models and tools... This might take a moment."):
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accent_tool_obj, analysis_agent, follow_up_agent = load_tool_and_agent()
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = []
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if hasattr(accent_tool_obj, "last_transcript") and accent_tool_obj.last_transcript:
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prompt_label = "Ask more about the video..."
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input_key = "followup"
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prompt_label = "Paste your prompt here..."
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input_key = "initial"
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user_input = st.chat_input(prompt_label, key=input_key)
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# Variable to defer assistant response
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deferred_response = None
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deferred_spinner_msg = ""
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if user_input:
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st.session_state.chat_history.append(HumanMessage(content=user_input))
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if re.search(r'https?://\S+', user_input):
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accent_tool_obj.last_transcript = ""
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deferred_spinner_msg = "Analyzing new video..."
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def run_agent():
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return analysis_agent.invoke(st.session_state.chat_history)[-1].content
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else:
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deferred_spinner_msg = "Responding based on transcript..."
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def run_agent():
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return follow_up_agent.invoke(st.session_state.chat_history).content
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# Run response generation inside spinner after chat is rendered
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def process_response():
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with st.spinner(deferred_spinner_msg):
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try:
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result = run_agent()
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except Exception as e:
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result = f"Error: {str(e)}"
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st.session_state.chat_history.append(AIMessage(content=result))
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st.rerun()
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# Display full chat history (before running spinner)
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for msg in st.session_state.chat_history:
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with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
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st.markdown(msg.content)
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# Only process response at the bottom, after chat is shown
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if user_input:
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process_response()
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