Update streamlit_app.py

streamlit_app.py

@@ -1,97 +1,16 @@
-# 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():
-#     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()
-
-# 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
-import os
-import re
-
 from src.tools.accent_tool import AccentAnalyzerTool
 from src.app.main_agent import create_agent
 from langchain_core.messages import HumanMessage, AIMessage
+import re
 
-# Page config
-st.set_page_config(
-    page_title="Accent Analyzer Agent",
-    page_icon="💬",
-    layout="centered"
-)
+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`")
 
-# Load tools and agents
 @st.cache_resource
 def load_tool_and_agent():
     with st.spinner("Loading AI models and tools... This might take a moment."):
@@ -103,15 +22,9 @@ def load_tool_and_agent():
 
 accent_tool_obj, analysis_agent, follow_up_agent = load_tool_and_agent()
 
-# Initialize session state variables
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
-if "pending_input" not in st.session_state:
-    st.session_state.pending_input = None
-if "pending_spinner_msg" not in st.session_state:
-    st.session_state.pending_spinner_msg = ""
 
-# Determine prompt type
 if hasattr(accent_tool_obj, "last_transcript") and accent_tool_obj.last_transcript:
     prompt_label = "Ask more about the video..."
     input_key = "followup"
@@ -119,37 +32,43 @@ else:
     prompt_label = "Paste your prompt here..."
     input_key = "initial"
 
-# Chat input field
 user_input = st.chat_input(prompt_label, key=input_key)
 
-# Handle new user input (just queue for now)
+# Variable to defer assistant response
+deferred_response = None
+deferred_spinner_msg = ""
+
 if user_input:
     st.session_state.chat_history.append(HumanMessage(content=user_input))
-    st.session_state.pending_input = user_input
+
     if re.search(r'https?://\S+', user_input):
         accent_tool_obj.last_transcript = ""
-        st.session_state.pending_spinner_msg = "Analyzing new video..."
+        deferred_spinner_msg = "Analyzing new video..."
+        def run_agent():
+            return analysis_agent.invoke(st.session_state.chat_history)[-1].content
     else:
-        st.session_state.pending_spinner_msg = "Responding based on transcript..."
-    st.rerun()
-
-# Display chat history
+        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)
 
-# If input is queued, process it now
-if st.session_state.pending_input:
-    with st.spinner(st.session_state.pending_spinner_msg):
-        try:
-            if re.search(r'https?://\S+', st.session_state.pending_input):
-                result = analysis_agent.invoke(st.session_state.chat_history)[-1].content
-            else:
-                result = follow_up_agent.invoke(st.session_state.chat_history).content
-        except Exception as e:
-            result = f"Error: {str(e)}"
-        st.session_state.chat_history.append(AIMessage(content=result))
-    # Clear flags
-    st.session_state.pending_input = None
-    st.session_state.pending_spinner_msg = ""
-    st.rerun()
+# Only process response at the bottom, after chat is shown
+if user_input:
+    process_response()
+
+
+