req pword

app.py

@@ -1,6 +1,7 @@
 # LC_Streaming.py
 
 
+
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.chat_models import ChatOpenAI
 from langchain.schema import HumanMessage
@@ -20,17 +21,47 @@ class StreamHandler(BaseCallbackHandler):
         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("### 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")
+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:
+        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)])
-#    llm_response = response.content
-#    st.markdown(llm_response)
+    if query and ask_button:
+        response = chat([HumanMessage(content=query)])

app_pword.py

@@ -0,0 +1,64 @@
+
+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

@@ -0,0 +1,33 @@
+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)