import os

LC_chat_w_search.py

@@ -1,44 +0,0 @@
-# LC_chat_w_search.py
-
-
-from langchain.callbacks import StreamlitCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.tools import DuckDuckGoSearchRun
-
-with st.sidebar:
-    openai_api_key = st.text_input("OpenAI API Key", key="langchain_search_api_key_openai", type="password")
-    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
-
-st.title("🔎 LangChain - Chat with search")
-
-"""
-In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
-Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
-"""
-
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [
-        {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
-    ]
-
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["content"])
-
-if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-        st.stop()
-
-    llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, streaming=True)
-    search = DuckDuckGoSearchRun(name="Search")
-    search_agent = initialize_agent([search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
-    with st.chat_message("assistant"):
-        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        st.write(response)

LC_non-streaming.py

@@ -1,24 +0,0 @@
-# working
-# Non-streaming chat.
-# langchain llm, NOT CHAT_LLM
-
-import streamlit as st
-from langchain.llms import OpenAI
-import os
-
-
-st.title('🦜🔗 Dev App')
-
-openai_api_key = os.environ["OPENAI_API_KEY"] #st.sidebar.text_input('OpenAI API Key')
-
-def generate_response(input_text):
-  llm = OpenAI(temperature=0.0, openai_api_key=openai_api_key)
-  st.info(llm(input_text))
-
-with st.form('my_form'):
-  text = st.text_area('Enter text:', 'What are the three key pieces of advice for learning how to code?')
-  submitted = st.form_submit_button('Submit')
-  if not openai_api_key.startswith('sk-'):
-    st.warning('Please enter your OpenAI API key!', icon='⚠')
-  if submitted and openai_api_key.startswith('sk-'):
-    generate_response(text)

LC_streaming.py

@@ -1,36 +0,0 @@
-# LC_Streaming.py
-
-
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import HumanMessage
-import streamlit as st
-
-class StreamHandler(BaseCallbackHandler):
-    def __init__(self, container, initial_text="", display_method='markdown'):
-        self.container = container
-        self.text = initial_text
-        self.display_method = display_method
-
-    def on_llm_new_token(self, token: str, **kwargs) -> None:
-        self.text += token + "/"
-        display_function = getattr(self.container, self.display_method, None)
-        if display_function is not None:
-            display_function(self.text)
-        else:
-            raise ValueError(f"Invalid display_method: {self.display_method}")
-
-query = st.text_input("input your query", value="Tell me a joke")
-ask_button = st.button("ask")
-
-st.markdown("### streaming box")
-chat_box = st.empty()
-stream_handler = StreamHandler(chat_box, display_method='write')
-chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[stream_handler])
-
-st.markdown("### together box")
-
-if query and ask_button:
-    response = chat([HumanMessage(content=query)])
-    llm_response = response.content
-    st.markdown(llm_response)

app.py

@@ -6,6 +6,8 @@ from langchain.callbacks.base import BaseCallbackHandler
 from langchain.chat_models import ChatOpenAI
 from langchain.schema import HumanMessage
 import streamlit as st
+import os
+
 
 class StreamHandler(BaseCallbackHandler):
     def __init__(self, container, initial_text="", display_method='markdown'):

app_hello_world.py

@@ -1,5 +0,0 @@
-import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-

app_pword.py

@@ -1,64 +0,0 @@
-
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import HumanMessage
-import streamlit as st
-
-class StreamHandler(BaseCallbackHandler):
-    def __init__(self, container, initial_text="", display_method='markdown'):
-        self.container = container
-        self.text = initial_text
-        self.display_method = display_method
-
-    def on_llm_new_token(self, token: str, **kwargs) -> None:
-        self.text += token # + "/"
-        display_function = getattr(self.container, self.display_method, None)
-        if display_function is not None:
-            display_function(self.text)
-        else:
-            raise ValueError(f"Invalid display_method: {self.display_method}")
-
-
-import streamlit as st
-
-def check_password():
-    """Returns `True` if the user had the correct password."""
-
-    def password_entered():
-        """Checks whether a password entered by the user is correct."""
-        if st.session_state["password"] == os.environ["USER_PWORD"]:
-            st.session_state["password_correct"] = True
-            del st.session_state["password"]  # don't store password
-        else:
-            st.session_state["password_correct"] = False
-
-    if "password_correct" not in st.session_state:
-        # First run, show input for password.
-        st.text_input(
-            "Password", type="password", on_change=password_entered, key="password"
-        )
-        return False
-    elif not st.session_state["password_correct"]:
-        # Password not correct, show input + error.
-        st.text_input(
-            "Password", type="password", on_change=password_entered, key="password"
-        )
-        st.error("😕 Password incorrect")
-        return False
-    else:
-        # Password correct.
-        return True
-
-if check_password():
-    query = st.text_input("Input your research question", value="How do biases in AI student evaluations compare to documented biases in human evaluations?")
-    ask_button = st.button("ask")
-
-    st.markdown("### GPT-3.5 response")
-    chat_box = st.empty()
-    stream_handler = StreamHandler(chat_box, display_method='write')
-    chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[stream_handler])
-
-    #st.markdown("### together box")
-
-    if query and ask_button:
-        response = chat([HumanMessage(content=query)])

lc_streaming_nopassword.py

@@ -1,33 +0,0 @@
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import HumanMessage
-import streamlit as st
-
-class StreamHandler(BaseCallbackHandler):
-    def __init__(self, container, initial_text="", display_method='markdown'):
-        self.container = container
-        self.text = initial_text
-        self.display_method = display_method
-
-    def on_llm_new_token(self, token: str, **kwargs) -> None:
-        self.text += token # + "/"
-        display_function = getattr(self.container, self.display_method, None)
-        if display_function is not None:
-            display_function(self.text)
-        else:
-            raise ValueError(f"Invalid display_method: {self.display_method}")
-
-query = st.text_input("Input your research question", value="How do biases in AI student evaluations compare to documented biases in human evaluations?")
-ask_button = st.button("ask")
-
-st.markdown("### GPT-3.5 response")
-chat_box = st.empty()
-stream_handler = StreamHandler(chat_box, display_method='write')
-chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[stream_handler])
-
-#st.markdown("### together box")
-
-if query and ask_button:
-    response = chat([HumanMessage(content=query)])
-#    llm_response = response.content
-#    st.markdown(llm_response)