Initial app code

app.py

@@ -0,0 +1,24 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "InvestmentResearchAI/LLM-ADE_tiny-v0.001"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_response(input_text):
+    """Generate response from the model based on the input text."""
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    output = model.generate(**inputs, max_length=512, num_return_sequences=1)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Streamlit interface
+st.title("IRAI LLM-ADE Model")
+user_input = st.text_area("Enter your text here:", "")
+if st.button("Generate"):
+    if user_input:
+        response = generate_response(user_input)
+        st.text_area("Model Response:", response, height=300)
+    else:
+        st.warning("Please enter some text to generate a response.")