streamlit_app.py

# import streamlit as st
# from src.tools.accent_tool import AccentAnalyzerTool
# from src.app.main_agent import create_agent
# from langchain_core.messages import HumanMessage, AIMessage
# import re

# st.set_page_config(page_title="Accent Analyzer Agent", page_icon="💬", layout="centered")

# st.warning("⚠️ High latency due to CPU usage. Once migrated to GPU, response time will improve significantly.")

# st.title("English Accent Analyzer (Conversational)")
# 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`")

# @st.cache_resource
# def load_tool_and_agent():
#     tool = AccentAnalyzerTool()
#     analysis_agent, follow_up_agent = create_agent(tool)
#     return tool, analysis_agent, follow_up_agent

# accent_tool_obj, analysis_agent, follow_up_agent = load_tool_and_agent()

# if "chat_history" not in st.session_state:
#     st.session_state.chat_history = []

# if hasattr(accent_tool_obj, "last_transcript") and accent_tool_obj.last_transcript:
#     prompt_label = "Ask more about the video..."
#     input_key = "followup"
# else:
#     prompt_label = "Paste your prompt here..."
#     input_key = "initial"

# user_input = st.chat_input(prompt_label, key=input_key)

# # Variable to defer assistant response
# deferred_response = None
# deferred_spinner_msg = ""

# if user_input:
#     st.session_state.chat_history.append(HumanMessage(content=user_input))

#     if re.search(r'https?://\S+', user_input):
#         accent_tool_obj.last_transcript = ""
#         deferred_spinner_msg = "Analyzing new video..."
#         def run_agent():
#             return analysis_agent.invoke(st.session_state.chat_history)[-1].content
#     else:
#         deferred_spinner_msg = "Responding based on transcript..."
#         def run_agent():
#             return follow_up_agent.invoke(st.session_state.chat_history).content

#     # Run response generation inside spinner after chat is rendered
#     def process_response():
#         with st.spinner(deferred_spinner_msg):
#             try:
#                 result = run_agent()
#             except Exception as e:
#                 result = f"Error: {str(e)}"
#             st.session_state.chat_history.append(AIMessage(content=result))
#             st.rerun() 

# # Display full chat history (before running spinner)
# for msg in st.session_state.chat_history:
#     with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
#         st.markdown(msg.content)

# # Only process response at the bottom, after chat is shown
# if user_input:
#     process_response()

import streamlit as st
from src.tools.accent_tool import AccentAnalyzerTool
from src.app.main_agent import create_agent
from langchain_core.messages import HumanMessage, AIMessage
import re
import os

st.set_page_config(page_title="Accent Analyzer Agent", page_icon="💬", layout="centered")

st.warning("⚠️ High latency due to CPU usage. Once migrated to GPU, response time will improve significantly.")
st.title("English Accent Analyzer (Conversational)")
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`")

if "chat_history" not in st.session_state:
    st.session_state.chat_history = [] 
if "last_transcript_available" not in st.session_state:
    st.session_state.last_transcript_available = False 
if "processing_input" not in st.session_state:
    st.session_state.processing_input = False 

@st.cache_resource
def load_tool_and_agent_cached():
    with st.spinner("Loading AI models and tools... This might take a moment."):
        if not os.environ.get("HF_TOKEN"):
            st.warning("HF_TOKEN environment variable not found. Some Hugging Face models might require authentication and may fail to load.")

        tool = AccentAnalyzerTool()
        analysis_agent, follow_up_agent = create_agent(tool)
        return tool, analysis_agent, follow_up_agent

accent_tool_obj, analysis_agent, follow_up_agent = load_tool_and_agent_cached()

if hasattr(accent_tool_obj, "last_transcript") and accent_tool_obj.last_transcript:
    st.session_state.last_transcript_available = True
else:
    st.session_state.last_transcript_available = False

prompt_label = "Ask more about the video..." if st.session_state.last_transcript_available else "Paste your prompt here..."
user_input = st.chat_input(prompt_label, disabled=st.session_state.processing_input)

if user_input and not st.session_state.processing_input:
    st.session_state.processing_input = True 
    st.session_state.chat_history.append(HumanMessage(content=user_input)) 

    if re.search(r'https?://\S+', user_input):
        accent_tool_obj.last_transcript = "" 
        st.session_state.last_transcript_available = False 
        agent_to_run = analysis_agent
        spinner_msg = "Analyzing new video and transcribing audio..."
    else:
        agent_to_run = follow_up_agent
        spinner_msg = "Generating response based on transcript..."
        
    with st.spinner(spinner_msg):
        try:
            if agent_to_run == analysis_agent:
                response_content = agent_to_run.invoke(st.session_state.chat_history)[-1].content
            else:
                response_content = follow_up_agent.invoke(st.session_state.chat_history).content

            if agent_to_run == analysis_agent and hasattr(accent_tool_obj, "last_transcript") and accent_tool_obj.last_transcript:
                st.session_state.last_transcript_available = True

        except Exception as e:
            response_content = f"Error: {str(e)}"
            st.error(f"An error occurred during processing: {e}")

    st.session_state.chat_history.append(AIMessage(content=response_content))
    st.session_state.processing_input = False
    st.rerun()

for msg in st.session_state.chat_history:
    with st.chat_message("user" if isinstance(msg, HumanMessage) else "assistant"):
        st.markdown(msg.content)

if not st.session_state.chat_history:
    st.info("Start by pasting a public MP4 video URL above!")