Update app.py

app.py

@@ -1,82 +1,83 @@
-import streamlit as st
-import random
-import time
-import os
-from langchain_together import ChatTogether
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import TextLoader
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_community.vectorstores import FAISS
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-from langchain_together import TogetherEmbeddings
-
-os.environ["TOGETHER_API_KEY"] = "6216ce36aadcb06c35436e7d6bbbc18b354d8140f6e805db485d70ecff4481d0"
-
-#load
-loader = TextLoader("Resume_data.txt")
-documents = loader.load()
-
-# split it into chunks
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-docs = text_splitter.split_documents(documents)
-vectorstore = FAISS.from_documents(docs,
-     TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
-)
-
-retriever = vectorstore.as_retriever()
-print("assigning model")
-model = ChatTogether(
-    model="meta-llama/Llama-3-70b-chat-hf",    
-    temperature=0.0,
-    max_tokens=500,)
-
-prompt = ChatPromptTemplate([
-    ("system", "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. answer as if person is responding. and if user greets then greet back"),
-    ("user", "context : {context}, Question: {question}")
-])
-
-chain = (
-    {"context": retriever, "question": RunnablePassthrough()}
-    | prompt
-    | model
-    | StrOutputParser()
-)
-
-
-st.title("Chat with me")
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Accept user input
-if prompt := st.chat_input("What is up?"):
-    # Display user message in chat message container
-    with st.chat_message("user"):
-        st.markdown(prompt)
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-############################################
-# Streamed response emulator
-def response_generator():
-    query = f"{prompt}"
-    if query != "None":
-        for m in chain.stream(query):
-            yield m
-            time.sleep(0.05)
-    else:
-        yield "How can i help you?"
-        
-###########################################
-# Display assistant response in chat message container
-with st.chat_message("assistant"):
-    response = st.write_stream(response_generator())
-# Add assistant response to chat history
+import streamlit as st
+import random
+import time
+import os
+from langchain_together import ChatTogether
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import TextLoader
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.vectorstores import FAISS
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_together import TogetherEmbeddings
+
+os.environ["TOGETHER_API_KEY"] = "bafbab854ae828c3b90f675c45c8263e9404d278b5694909ea0855f437b9d1f3"
+# "6216ce36aadcb06c35436e7d6bbbc18b354d8140f6e805db485d70ecff4481d0"
+
+#load
+loader = TextLoader("Resume_data.txt")
+documents = loader.load()
+
+# split it into chunks
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+vectorstore = FAISS.from_documents(docs,
+     TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
+)
+
+retriever = vectorstore.as_retriever()
+print("assigning model")
+model = ChatTogether(
+    model="meta-llama/Llama-3-70b-chat-hf",    
+    temperature=0.0,
+    max_tokens=500,)
+
+prompt = ChatPromptTemplate([
+    ("system", "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. answer as if person is responding. and if user greets then greet back"),
+    ("user", "context : {context}, Question: {question}")
+])
+
+chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+st.title("Chat with me")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+############################################
+# Streamed response emulator
+def response_generator():
+    query = f"{prompt}"
+    if query != "None":
+        for m in chain.stream(query):
+            yield m
+            time.sleep(0.05)
+    else:
+        yield "How can i help you?"
+        
+###########################################
+# Display assistant response in chat message container
+with st.chat_message("assistant"):
+    response = st.write_stream(response_generator())
+# Add assistant response to chat history
 st.session_state.messages.append({"role": "assistant", "content": response})