Update app.py

app.py

@@ -1,32 +1,54 @@
-# in new conda env install below pakages
-# pip install tensorflow==2.13.0
-# pip install -U "tensorflow-text==2.13.*"
-# pip install -q streamlit==1.26.0
-# pip install openai==0.28.0
 import os
+import time
 import openai
+import numpy as np
 import streamlit as st
 import tensorflow as tf
 import tensorflow_text
-import numpy as np
+# import plotly.graph_objects as go
+# from dotenv import load_dotenv
+from langchain.llms import OpenAI
+from langchain.memory import ConversationSummaryMemory, ChatMessageHistory
+from llm import sys_instruction
+
+
+##############
+# PAGE STYLES
 
-###############################################
-# Setting up styles for app
-###############################################
 # Set page title and icon
-# st.set_page_config(page_title="Bard ChatBot",
-#                    page_icon=":robot_face:",
-#                    initial_sidebar_state="collapsed",)
+st.set_page_config(page_title="EmoInsight",
+                   page_icon=":robot_face:",
+                   initial_sidebar_state="expanded",)
+
+# Custom css styles
+with open('style.css') as f:
+    st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
 
-# # Custom css styles
-# with open('style.css') as f:
-#     st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+# Load variables from .env file
+# load_dotenv()
 
+# Load large model
 
-# st.title("ChatGPT-like clone")
 
-openai.api_key = os.environ['API_TOKEN']
-reloaded_model = tf.saved_model.load('one_2')
+@st.cache_resource  # Decorator to cache non-data objects
+def Loading_sentiment_analysis_model():
+    model = tf.saved_model.load('one_2')
+    return model
+
+
+senti_model = Loading_sentiment_analysis_model()
+
+
+emoji_mapping = {
+    "sadness": "😢",
+    "neutral": "😐",
+    "joy": "😄",
+    "anger": "😡",
+    "fear": "😨",
+    "love": "❤️",
+    "surprise": "😲",
+}
 
 emotion_categories = {
     0: 'anger',
@@ -38,63 +60,165 @@ emotion_categories = {
     6: 'surprise'
 }
 
-if "openai_model" not in st.session_state:
-    st.session_state["openai_model"] = "gpt-3.5-turbo"
 
-if "messages" not in st.session_state:
-    st.session_state.messages = []
+##################
+# STATE VARIABLES
 
-# col1, col2 = st.columns([4, 3])
+# set api key
+if 'key' not in st.session_state:
+    st.session_state.key = os.environ["API_TOKEN"]
 
-# with col1:
-#     st.markdown('helow')
-#     for message in st.session_state.messages:
-#         with st.chat_message(message["role"]):
-#             st.markdown(message["content"])
+openai.api_key = st.session_state.key
 
+# gpt llm
+if 'llm' not in st.session_state:
+    st.session_state.llm = OpenAI(
+        temperature=0.2, openai_api_key=st.session_state.key)
 
-# with col2:
-    # a = st.session_state.messages[-1]['content']
-    # st.markdown(len(st.session_state.messages) > 1)
-    # if len(st.session_state.messages) != 0:
-    # st.markdown('prediction:')
-    # st.markdown(st.session_state.messages[-2])
-    # q = st.session_state.messages[-2]['content']
-    # emotion = reloaded_model([q])
-    # true_classes = np.argmax(emotion, axis=1)
-    # emotion_category = emotion_categories.get(int(true_classes))
-    # st.markdown(emotion_category)
+# model name
+if "openai_model" not in st.session_state:
+    st.session_state["openai_model"] = "gpt-3.5-turbo"
 
-st.markdown('Chat Bot')
+# st chat history
+if "message_history" not in st.session_state:
+    st.session_state.message_history = []
+
+# set instruction for gpt response
+if 'sys_inst' not in st.session_state:
+    st.session_state.sys_inst = sys_instruction()
+
+# dict to store user question emotion
+if 'emotion_counts' not in st.session_state:
+    st.session_state.emotion_counts = {
+        'anger': 0,
+        'fear': 0,
+        'joy': 0,
+        'love': 0,
+        'neutral': 0,
+        'sadness': 0,
+        'surprise': 0
+    }
+
+
+#######################
+# LANG-CHAIN VARIABLES
+
+# storing chat history
+if 'old_summary' not in st.session_state:
+    st.session_state.old_summary = 'User came to psychological assistant chatbot'
+
+# langChian msg history
+if 'lg_msg_history' not in st.session_state:
+    st.session_state.lg_msg_history = ChatMessageHistory()
+
+# summarize old conversation
+if 'memory' not in st.session_state:
+    st.session_state.memory = ConversationSummaryMemory.from_messages(
+        llm=st.session_state.llm,
+        buffer=st.session_state.old_summary,
+        return_messages=True,
+        chat_memory=st.session_state.lg_msg_history)
+
+
+#############################################
+#                MAIN APP                   #
+#############################################
+st.sidebar.markdown('')
+st.sidebar.markdown('')
+st.sidebar.markdown('')
+st.sidebar.success("Select `Sentiment Plot` button to see the Emotino Graph")
+st.sidebar.markdown('')
+clear_chats = st.sidebar.button('Clear Chat')
+
+if clear_chats:
+    st.session_state.lg_msg_history.clear()
+    st.session_state.old_summary = 'User came to psychological assistant chatbot'
+    st.session_state.message_history = []
+    alert = st.sidebar.warning('Chat cleared', icon='🚨')
+    time.sleep(2)  # Wait for 3 seconds
+    alert.empty()  # Clear the alert
+
+
+st.markdown("<h1><center>EmoInsight</center></h1>",
+            unsafe_allow_html=True)
+
+
+# greetings
+if len(st.session_state.message_history) == 0:
+    # add to st history
+    st.session_state.message_history.append(
+        {"role": "assistant", "content": "How can I help you?"})
+    # add to lg history
+    # st.session_state.lg_msg_history.add_ai_message("How can I help you?")
+
+# HISTORY
+for message in st.session_state.message_history:
+    if message['role'] == 'system':
+        with st.chat_message("Emotion", avatar=emoji_mapping.get(message["content"])):
+            a = "Sentiment: {}".format(message["content"])
+            st.markdown(a)
+    else:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+# CHAT BOT
 if prompt := st.chat_input("What is up?"):
-    st.session_state.messages.append({"role": "user", "content": prompt})
     # USER
     with st.chat_message("user"):
         st.markdown(prompt)
+    # add to st history
+    st.session_state.message_history.append(
+        {"role": "user", "content": prompt})
+    # add to lg history
+    st.session_state.lg_msg_history.add_user_message(prompt)
+
+    # SENTIMENT PREDICION
+    emotion = senti_model([prompt])
+    true_classes = np.argmax(emotion, axis=1)
+    emotion_category = emotion_categories.get(int(true_classes))
+    st.session_state.emotion_counts[emotion_category] += 1
 
     # EMOTION
-    with st.chat_message("Emotion", avatar='😶'):
-        emotion = reloaded_model([prompt])
-        true_classes = np.argmax(emotion, axis=1)
-        emotion_category = emotion_categories.get(int(true_classes))
-        st.write("Emotion: {}".format(emotion_category))
+    with st.chat_message("Emotion", avatar=emoji_mapping.get(emotion_category)):
+        st.write("Sentiment: {}".format(emotion_category))
+        st.session_state.message_history.append(
+            {"role": "system", "content": emotion_category})
 
     # AI BOT
     with st.chat_message("assistant"):
         message_placeholder = st.empty()
         full_response = ""
+
+        # get response
         for response in openai.ChatCompletion.create(
-            model=st.session_state["openai_model"],
-            messages=[
-                {"role": m["role"], "content": m["content"]}
-                for m in st.session_state.messages
-            ],
-            stream=True,
-        ):
+                model=st.session_state["openai_model"],
+                messages=[
+                    {"role": "system", "content": st.session_state.sys_inst.format(
+                        history=st.session_state.old_summary)},
+                    {"role": "user", "content": prompt}
+                ],  # pass old chat history
+                stream=True):
+
+            # render gpt response in realtime
             full_response += response.choices[0].delta.get("content", "")
-
             message_placeholder.markdown(full_response + "▌")
-
         message_placeholder.markdown(full_response)
-    st.session_state.messages.append(
+
+    # add to st history
+    st.session_state.message_history.append(
         {"role": "assistant", "content": full_response})
+    # add to lg history
+    st.session_state.lg_msg_history.add_ai_message(prompt)
+
+# Clear old chat after 4 dialogs
+# And update old summary with new summary
+chat_len = len(st.session_state.lg_msg_history.messages)
+if (chat_len >= 4) and (chat_len % 4 == 0):
+
+    # get new summary of chat
+    st.session_state.old_summary = st.session_state.memory.predict_new_summary(
+        messages=st.session_state.lg_msg_history.messages,
+        existing_summary=st.session_state.old_summary)
+
+    # flush old lg-chat history
+    st.session_state.lg_msg_history.clear()