Create app.py

app.py

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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.")