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app.py

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+import asyncio
+import streamlit as st
+from langchain.llms import HuggingFaceHub
+from langchain.chains import ConversationChain
+import os
+from langchain.chains.conversation.memory import ConversationBufferMemory
+from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
+# os.environ['HUGGING_FACE_HUB_API_KEY']
+
+st.sidebar.title("Welcome Wanderers", help='This is just a beta model, and is still in progress!!!')
+# Add an image to the sidebar
+st.sidebar.image("assets/chatbot.jpg") 
+
+st.sidebar.divider()
+
+# Create a sidebar dropdown
+selected_option = st.sidebar.selectbox("Select Model:", ["lmsys/fastchat-t5-3b-v1.0", "google/flan-t5-base",])
+
+# Display the selected option below the dropdown
+# st.sidebar.write("Model : ", selected_option)
+
+st.sidebar.divider()
+
+max_length = st.sidebar.slider("Max Length", value=132, min_value=32, max_value=250)
+temperature = st.sidebar.slider("Temperature", value=0.60, min_value=0.0, max_value=1.0, step=0.05)
+
+repo_id = selected_option
+llm = HuggingFaceHub(
+    huggingfacehub_api_token=os.environ['HUGGING_FACE_HUB_API_KEY'],
+    repo_id=repo_id,
+    model_kwargs={
+        'temperature': temperature,
+        'max_length': max_length,
+    }
+)
+
+memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=80)
+Conversation_buf = ConversationChain(
+    llm=llm,
+    memory=memory
+)
+
+st.markdown("<h1 style='text-align: center;'>Chat Application 🚀🤖</h1>", unsafe_allow_html=True)
+
+st.divider()
+default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by \nthe Me using HuggingFace Models, Its like having a smart machine that completes \nyour thoughts 😀 Get started by typing a custom snippet, check out the repository, \nor try one of the examples. Have fun!"
+st.text(default_value)
+
+st.divider()
+
+# Create a placeholder for the conversation history
+conversation_history_placeholder = st.empty()
+
+# Create a list to store the conversation history
+conversation_history = []
+
+user_input = st.text_input("Your Query", max_chars=2024)
+
+if st.button("Predict"):
+    # Append user input to the conversation history
+    conversation_history.insert(0 ,f"User: {user_input}")
+    
+    # Await the coroutine to get the actual text
+    prediction = asyncio.run(Conversation_buf.acall(inputs=user_input))
+    keys_list = list(prediction.items())
+    keys = keys_list[2]
+    response = keys[1][5:]
+    
+    # Append model response to the conversation history
+    conversation_history.insert(1, f"Mr.Zhongli: {response}")
+
+    # Update the conversation history placeholder
+    conversation_history_placeholder.text_area("Conversation...", "\n".join(conversation_history), height=200)
+
+    # st.text(memory.buffer)