Create langchain_app.py

langchain_app.py

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+import streamlit as st
+from langchain.chains import ConversationChain
+from hugchat import hugchat
+from hugchat.login import Login
+
+st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
+st.title('🤗💬 HugChat App')
+
+# Hugging Face Credentials
+with st.sidebar:
+    st.header('Hugging Face Login')
+    hf_email = st.text_input('Enter E-mail:', type='password')
+    hf_pass = st.text_input('Enter password:', type='password')
+
+# Store AI generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "I'm HugChat, How may I help you?"}]
+
+# Display existing chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# Function for generating LLM response
+def generate_response(prompt, email, passwd):
+    # Hugging Face Login
+    sign = Login(email, passwd)
+    cookies = sign.login()
+    sign.saveCookies()
+    # Create ChatBot                        
+    chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+    chain = ConversationChain(llm=chatbot)
+    response = chain.run(input=prompt)
+    return response
+
+# Prompt for user input and save
+if prompt := st.chat_input():
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+# If last message is not from assistant, we need to generate a new response
+if st.session_state.messages[-1]["role"] != "assistant":
+    # Call LLM
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = generate_response(prompt, hf_email, hf_pass)
+            st.write(response)
+            
+    message = {"role": "assistant", "content": response}
+    st.session_state.messages.append(message)