app.py

import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
import torch

from huggingface_hub import HfApi




#Sidebar menu
st.sidebar.title('Menu')

home = st.sidebar.checkbox("Home")
time_series = st.sidebar.checkbox('Time Series Data')
chatbot = st.sidebar.checkbox('Chatbot')

if home:
    st.title("Food Security in Africa and Asia")
    st.text("Hi there! I'm your food security assistant. Food security means everyone has access to safe, nutritious food to meet their dietary needs.\n" 
        "Want to learn more about food insecurity, its causes, or potential solutions?")

if time_series:
    st.header("Time series data from 2000 to 2022")
    st.text("This data was collected from trusted organizations and depict metrcis on food security based on climate change and food produduced")


if chatbot:
    
    st.header("Chat with me.")
    text = st.text_area("Food security is a global challenge. Let's work together to find solutions. How can I help you today?")

  
    tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
    model = AutoModelForQuestionAnswering.from_pretrained(
    "google/gemma-2-9b-it",
    device_map="auto",
    torch_dtype=torch.bfloat16)
    
   
    
    if text:
        input_ids = tokenizer(text, return_tensors="pt").to("cuda")
        outputs = model.generate(**input_ids)
        st.write(tokenizer.decode(outputs[0]))




'''
if chatbot:
     
    st.header("Chat with me.")
    text = st.text_area("Food security is a global challenge. Let's work together to find solutions. How can I help you today?")

    pipe = pipeline("question-answering", model=model)
    
    if text:
        out = pipe(text)
        st.write(out)

'''