Update streamlit_app.py

streamlit_app.py

@@ -1,5 +1,70 @@
+import google.generativeai as genai
 import streamlit as st
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
 
-st.title('🎈 App Name')
+# API configuration
+genai.configure(api_key="AIzaSyAAHxBqPkHCxw1Y4zIGXkMSUKt11EsjI8c")
 
-st.write('Hello world!')
+# Generation configuration
+generation_config = {
+    "temperature": 0,
+    "top_p": 0.95,
+    "top_k": 64,
+    "max_output_tokens": 200,
+    "response_mime_type": "text/plain",
+}
+
+# Create the model
+model = genai.GenerativeModel(
+    model_name="gemini-1.5-flash",
+    generation_config=generation_config,  # type: ignore
+)
+
+# Initialize the conversation history
+history = []
+
+# Function to interact with the model and return a response
+def gemini_1(query):
+    chat_session = model.start_chat(
+        history=history
+    )
+    response = chat_session.send_message(query)
+    model_response = response.text
+    # Update conversation history
+    history.append({"role": "user", "parts": [query]})
+    history.append({"role": "model", "parts": [model_response]})
+    return model_response
+
+# Streamlit UI setup
+st.title("Welcome to Aushadi_veda_Chat_Bot")
+
+# Sidebar for additional information or settings
+with st.sidebar:
+    st.header("Aushadi_veda", divider="rainbow")
+    st.write("Welcome to the Aushadi_veda_Chat_Bot AI chat interface!")
+    st.write("Ask me anything, and I'll provide responses based on your queries.")
+
+# Initialize session state for storing conversation messages
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display previous conversation messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Input area for the user to send messages
+prompt = st.chat_input("How can I assist you today?")
+
+# If the user submits a query, process it and get a response
+if prompt:
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    # Get the response from the model
+    response = gemini_1(prompt)
+
+    with st.chat_message("assistant"):
+        st.markdown(response)
+        st.session_state.messages.append({"role":"assistant", "content":response})