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import streamlit as st
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load your dataset from Hugging Face
dataset = load_dataset("diylocals/TestData") # Replace with your actual username and dataset name
# Load the IBM Granite model and tokenizer
model_name = "ibm-granite/granite-3.0-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Streamlit app title
st.title("IBM Granite Model Analysis")
# Input text area for user input
user_input = st.text_area("Enter text for analysis (e.g., voltage readings):", "")
if st.button("Analyze"):
if user_input:
# Prepare input for the model
inputs = tokenizer(user_input, return_tensors="pt")
# Generate output using the model
outputs = model.generate(**inputs)
# Decode and display output
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write("Model Output:")
st.write(output_text)
else:
st.warning("Please enter some text for analysis.") |