Update app.py

app.py

@@ -1,8 +1,14 @@
-import streamlit as st
-from langchain.chains import ConversationChain
+import sys
+sys.path.append("/home/ece/hdd/divyanshu/Bihar")
+
+from model import get_llm, CustomLLM
+from langchain import PromptTemplate
+from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE, DEFAULT_TEMPLATE
 from langchain.chains.conversation.memory import ConversationEntityMemory
-from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
-from model import get_llm
+from langchain.chains import ConversationChain
+import streamlit as st
+
+
 
 st.set_page_config(page_title='Bihar Now & Then', layout='wide')
 
@@ -15,14 +21,14 @@ if "input" not in st.session_state:
 if "stored_session" not in st.session_state:
     st.session_state["stored_session"] = []
 
+
 def get_text():
 
     input_text = st.text_input("You: ", st.session_state["input"], key="input",
-                            placeholder="Ask me anything related to Bihar ...", 
-                            label_visibility='hidden')
+                               placeholder="Ask me anything related to Bihar ...",
+                               label_visibility='hidden')
     return input_text
 
-# Define function to start a new chat
 def new_chat():
     """
     Clears session state and starts a new chat.
@@ -30,7 +36,7 @@ def new_chat():
     save = []
     for i in range(len(st.session_state['generated'])-1, -1, -1):
         save.append("User:" + st.session_state["past"][i])
-        save.append("Bot:" + st.session_state["generated"][i])        
+        save.append("Bot:" + st.session_state["generated"][i])
     st.session_state["stored_session"].append(save)
     st.session_state["generated"] = []
     st.session_state["past"] = []
@@ -38,47 +44,49 @@ def new_chat():
     st.session_state.entity_memory.entity_store = {}
     st.session_state.entity_memory.buffer.clear()
 
+
 # Set up sidebar with various options
 with st.sidebar.expander("🛠️ ", expanded=False):
-    # Option to preview memory store
     if st.checkbox("Preview memory store"):
         with st.expander("Memory-Store", expanded=False):
             st.session_state.entity_memory.store
-    # Option to preview memory buffer
     if st.checkbox("Preview memory buffer"):
         with st.expander("Bufffer-Store", expanded=False):
             st.session_state.entity_memory.buffer
-    MODEL = st.selectbox(label='Model', options=['gpt-3.5-turbo','text-davinci-003','text-davinci-002','code-davinci-002'])
-    K = st.number_input(' (#)Summary of prompts to consider',min_value=3,max_value=1000)
+    MODEL = st.selectbox(label='Model', options=["databricks/dolly-v2-3b"])
+    K = st.number_input(' (#)Summary of prompts to consider',
+                        min_value=3, max_value=1000)
 
-# Set up the Streamlit app layout
+st.subheader("Bihar Now Then")
 
-st.subheader(" Powered by 🦜 LangChain + 🤗 HuggingFace + Streamlit")
-
-model_name = "bert-large-uncased"
+model_name = "t5-large"
 pinecone_index = "bert-large-uncased"
 llm = "databricks/dolly-v2-3b"
-llm_chain, docsearch = get_llm(model_name,pinecone_index,llm)
+llm_chain, docsearch= get_llm(model_name, pinecone_index, llm)
+
+llm = CustomLLM()
 # Create a ConversationEntityMemory object if not already created
 if 'entity_memory' not in st.session_state:
-        st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K )
-    
-    # Create the ConversationChain object with the specified configuration
-Conversation = ConversationChain(
-        llm=llm, 
-        prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
-        memory=st.session_state.entity_memory
-    )  
+    st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K)
 
+Conversation = ConversationChain(
+    llm=llm,
+    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
+    memory=st.session_state.entity_memory
+)
 
-st.sidebar.button("New Chat", on_click = new_chat, type='primary')
+st.sidebar.button("New Chat", on_click=new_chat, type='primary')
 
 user_input = get_text()
 
 if user_input:
-    output = Conversation.run(input=user_input)  
-    st.session_state.past.append(user_input)  
-    st.session_state.generated.append(output)  
+    context = docsearch.similarity_search(user_input, k=3, metadata=False)
+    content = ""
+    for i in context:
+        content = content + f"{i.__dict__['page_content']}"
+    output = Conversation.run(entities=content, input=user_input)
+    st.session_state.past.append(user_input)
+    st.session_state.generated.append(output)
 
 # Allow to download as well
 download_str = []
@@ -89,18 +97,18 @@ with st.expander("Conversation", expanded=True):
         st.success(st.session_state["generated"][i])
         download_str.append(st.session_state["past"][i])
         download_str.append(st.session_state["generated"][i])
-    
+
     # Can throw error - requires fix
     download_str = '\n'.join(download_str)
     if download_str:
-        st.download_button('Download',download_str)
+        st.download_button('Download', download_str)
 
 # Display stored conversation sessions in the sidebar
 for i, sublist in enumerate(st.session_state.stored_session):
-        with st.sidebar.expander(label= f"Conversation-Session:{i}"):
-            st.write(sublist)
+    with st.sidebar.expander(label=f"Conversation-Session:{i}"):
+        st.write(sublist)
 
 # Allow the user to clear all stored conversation sessions
-if st.session_state.stored_session:   
+if st.session_state.stored_session:
     if st.sidebar.checkbox("Clear-all"):
-        del st.session_state.stored_session
+        del st.session_state.stored_session