Update app.py

app.py

@@ -1,83 +1,87 @@
-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,)
-
-# template = """<s>[INST] answer from context only as if person is responding (use i instead of you in response). and always answer in short answer.
-# answer for asked question only, if he greets greet back.
-template = """
-{context}
-Question: {question} [/INST] 
-"""
-prompt = ChatPromptTemplate.from_template(template) 
-
-chain = (
-    {"context": retriever, "question": RunnablePassthrough()}
-    | prompt
-    | model
-    | StrOutputParser()
-)
-
-
-st.title("Simple chat")
-
-# 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"echo {prompt}"
-    # for m in chain.stream(query):
-    #     print(m)
-    #     yield m + " "
-    #     time.sleep(0.05)
-    return chain.invoke(query)
-###########################################
-# Display assistant response in chat message container
-with st.chat_message("assistant"):
-    response = st.markdown(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"] = "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,)
+
+# template = """<s>[INST] answer from context only as if person is responding (use i instead of you in response). and always answer in short answer.
+# answer for asked question only, if he greets greet back.
+template = """
+{context}
+Question: {question} [/INST] 
+"""
+prompt = ChatPromptTemplate.from_template(template) 
+
+chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+st.title("Simple chat")
+
+# 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"echo {prompt}"
+    if query != "":
+        for m in chain.stream(query):
+            print(m)
+            yield m + " "
+    else:
+        yield "How can i help you?"
+        
+            # time.sleep(0.05)
+    # return chain.invoke(query)
+###########################################
+# 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})