Update app.py

app.py

@@ -1,4 +1,4 @@
-
+import openai
 import streamlit as st
 from langchain.llms import OpenAI
 from langchain.chat_models import ChatOpenAI
@@ -10,17 +10,11 @@ from langchain.prompts.prompt import PromptTemplate
 from langchain.vectorstores import FAISS
 import re
 import time
-# class CustomRetrievalQAWithSourcesChain(RetrievalQAWithSourcesChain):
-#         def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
-#             # Call the parent class's method to get the documents
-#             docs = super()._get_docs(inputs)
-#             # Modify the document metadata
-#             for doc in docs:
-#                 doc.metadata['source'] = doc.metadata.pop('path')
-#             return docs
 
+
+# import e5-large-v2 embedding model
 model_name = "intfloat/e5-large-v2"
-model_kwargs = {'device': 'cpu'}
+model_kwargs = {'device': 'cuda'}
 encode_kwargs = {'normalize_embeddings': False}
 embeddings = HuggingFaceEmbeddings(
     model_name=model_name,
@@ -28,6 +22,7 @@ embeddings = HuggingFaceEmbeddings(
     encode_kwargs=encode_kwargs
 )
 
+# load IPCC database
 db = FAISS.load_local("IPCC_index_e5_1000_pdf", embeddings)
 
 
@@ -107,14 +102,16 @@ def generate_response(input_text):
 with st.sidebar:
     openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password")
     "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-
-st.title("💬🌍🌡️Ask question about Climate Change")
-st.caption("🚀 A Climate Change chatbot powered by OpenAI LLM")
+    st.markdown("## 🌍 Welcome to ClimateChat! 🌍")
+    st.markdown("ClimateChat Harnesses the latest [IPCC reports](https://www.ipcc.ch/report/ar6/wg3/) and the power of Large Language Models to answer your questions about climate change. When you interact with ClimateChat not only will you receive clear, concise, and accurate answers, but each response is coupled with sources and hyperlinks for further exploration and verification.\
+                Our objective is to make climate change information accessible, understandable, and actionable for everyone, everywhere.")
+st.title("💬🌍🌡️ClimateChat")
+st.caption("💬 A Climate Change chatbot powered by OpenAI LLM and IPCC documents")
 #col1, col2,  = st.columns(2)
 
 
 if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "I'm a Chatbot who can answer your questions about the climate change!"}]
+    st.session_state["messages"] = [{"role": "assistant", "content": "Any question about the climate change?"}]
 
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
@@ -136,8 +133,27 @@ if prompt := st.chat_input():
         highlighted_text = match.group(1)
     else:
         highlighted_text="hello world"
-    st.session_state.messages.append({"role": "assistant", "content": result["result"]})
-    st.chat_message("assistant").write(result_r)
+
+
+
+
+        # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        assistant_response = result_r
+        # Simulate stream of response with milliseconds delay
+        for chunk in assistant_response.split():
+            full_response += chunk + " "
+            time.sleep(0.05)
+            # Add a blinking cursor to simulate typing
+            message_placeholder.write(full_response + "▌")
+        message_placeholder.write(result_r)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": result_r})
+        
+    #st.session_state.messages.append({"role": "assistant", "content": result["result"]})
+    #st.chat_message("assistant").write(result_r)
     #display_typing_effect(st.chat_message("assistant"), result_r)
     #st.markdown(result['source_documents'][0])
     #st.markdown(result['source_documents'][1])
@@ -146,4 +162,4 @@ if prompt := st.chat_input():
     #st.markdown(result['source_documents'][4])
 
 
-    st.image("https://cataas.com/cat/says/"+highlighted_text)
+    #st.image("https://cataas.com/cat/says/"+highlighted_text)