Create streamlit_app.py

streamlit_app.py

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+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.title("English Accent Analyzer (Conversational)")
+st.subheader("Ask me to analyze a video URL, e.g.: \n\n `Analyze this video: https://your-link.com/video.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()