Rename app..py to app.py

app..py → app.py

@@ -1,51 +1,51 @@
-import os
-import streamlit as st
-from langchain_core.messages import HumanMessage
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-# Set the Google API Key environment variable
-os.environ['GOOGLE_API_KEY'] = 'AIzaSyBBcjZ_uwmGWgDj77_-_Z019UhkDgW5Urg'
-
-def generate_chat_message(prompt):
-    model = ChatGoogleGenerativeAI(model="gemini-pro")
-    message = HumanMessage(content=prompt)
-    response = model.stream([message])
-
-    response_text = ""
-    for chunk in response:
-        response_text += chunk.content
-    return response_text
-
-def submit_input():
-    st.session_state.chat_history = []
-    user_input = st.session_state.user_input
-    if user_input:
-        # Append user message to chat history
-        st.session_state.chat_history.append(("User", user_input))
-
-        # Generate response from the model
-        response = generate_chat_message(user_input)
-        
-        # Append model response to chat history
-        st.session_state.chat_history.append(("AI", response))
-
-        # Clear the input box by setting session state
-        st.session_state.user_input = ""
-
-# Streamlit app layout
-st.title("Chat with LLM")
-
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
-# Input text box for the user
-st.text_input("You:", key="user_input", on_change=submit_input)
-
-# Display the chat history
-for sender, message in st.session_state.chat_history:
-    if sender == "User":
-        st.write(f"**You:** {message}")
-    else:
-        st.write(f"**AI:** {message}")
-
-print(st.session_state)
+import os
+import streamlit as st
+from langchain_core.messages import HumanMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+# Set the Google API Key environment variable
+os.environ['GOOGLE_API_KEY'] = '********************************'
+
+def generate_chat_message(prompt):
+    model = ChatGoogleGenerativeAI(model="gemini-pro")
+    message = HumanMessage(content=prompt)
+    response = model.stream([message])
+
+    response_text = ""
+    for chunk in response:
+        response_text += chunk.content
+    return response_text
+
+def submit_input():
+    st.session_state.chat_history = []
+    user_input = st.session_state.user_input
+    if user_input:
+        # Append user message to chat history
+        st.session_state.chat_history.append(("User", user_input))
+
+        # Generate response from the model
+        response = generate_chat_message(user_input)
+        
+        # Append model response to chat history
+        st.session_state.chat_history.append(("AI", response))
+
+        # Clear the input box by setting session state
+        st.session_state.user_input = ""
+
+# Streamlit app layout
+st.title("Chat with LLM")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Input text box for the user
+st.text_input("You:", key="user_input", on_change=submit_input)
+
+# Display the chat history
+for sender, message in st.session_state.chat_history:
+    if sender == "User":
+        st.write(f"**You:** {message}")
+    else:
+        st.write(f"**AI:** {message}")
+
+print(st.session_state)