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stream.py
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# from dotenv import load_dotenv
# import os
# import streamlit as st
# import google.generativeai as genai
# # Load environment variables
# load_dotenv()
# # Set up Streamlit title
# st.title("Agri Bot")
# # Configure Google Generative AI
# genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# # Initialize session state variables
# if "openai_model" not in st.session_state:
# st.session_state["openai_model"] = "gpt-3.5-turbo"
# if "messages" not in st.session_state:
# st.session_state.messages = []
# # Display previous messages
# for message in st.session_state.messages:
# with st.container():
# if message["role"] == "user":
# st.text_input("User:", value=message["content"], key=str(message))
# elif message["role"] == "assistant":
# st.text_area("Assistant:", value=message["content"], key=str(message))
# # Chat input
# prompt = st.text_input("User:", "What is up?")
# # Add user message to session state
# if prompt:
# st.session_state.messages.append({"role": "user", "content": prompt})
# # Generate response
# if prompt:
# messages = [{"role": m["role"], "content": m["content"]} for m in st.session_state.messages]
# stream = genai.chat.completions.create(model=st.session_state["openai_model"], messages=messages, stream=True)
# response = next(stream)
# st.session_state.messages.append({"role": "assistant", "content": response["choices"][0]["message"]["content"]})
# from dotenv import load_dotenv
# import os
# import streamlit as st
# import google.generativeai as genai
# # Load environment variables
# load_dotenv()
# # Set up Streamlit title
# st.title("Agri Bot")
# # Configure Google Generative AI
# genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# # Initialize session state variables
# if "openai_model" not in st.session_state:
# st.session_state["openai_model"] = "text-bison-001" # Using the "text-bison-001" model
# if "messages" not in st.session_state:
# st.session_state.messages = []
# # Display previous messages
# for message in st.session_state.messages:
# with st.container():
# if message["role"] == "user":
# st.markdown(f"**User:** {message['content']}") # Using markdown for better formatting
# elif message["role"] == "assistant":
# st.markdown(f"**Assistant:** {message['content']}") # Using markdown for better formatting
# # Chat input
# prompt = st.text_input("You:", placeholder="Ask me anything about agriculture...") # Added a placeholder
# # Add user message to session state
# if prompt:
# st.session_state.messages.append({"role": "user", "content": prompt})
# # Generate response
# if prompt:
# messages = [{"role": m["role"], "content": m["content"]} for m in st.session_state.messages]
# stream = genai.chat.completions.create(model=st.session_state["openai_model"], messages=messages, stream=True)
# response = next(stream)
# st.session_state.messages.append({"role": "assistant", "content": response["choices"][0]["message"]["content"]})
# # Display the response
# st.success(response["choices"][0]["message"]["content"]) # Using st.success for better visibility
from dotenv import load_dotenv
import os
import streamlit as st
import google.generativeai as genai
# Load environment variables
load_dotenv()
# Set up Streamlit title
st.title("Agri Bot")
st.subheader("Hey I'm your Chatbot for agriculture purposes, Tell me how can I help you....")
# Configure Google Generative AI
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = "models/text-bison-001" # Use the "text-bison-001" model
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("What is up?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
conversation_history = "\n".join(
[f"{m['role'].capitalize()}: {m['content']}" for m in st.session_state.messages]
)
response_completion = genai.generate_text(
model=st.session_state["openai_model"],
prompt=conversation_history,
)
response = response_completion.result
# Display the response in chunks
for chunk in response.split("\n\n"):
st.write(chunk)
st.session_state.messages.append({"role": "assistant", "content": response})