Create app.py

app.py

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+import streamlit as st
+import logging
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Set the logger to display only CRITICAL messages
+logging.basicConfig(level=logging.CRITICAL)
+
+
+# Cache the model and tokenizer to avoid reloading it every time
+@st.experimental_singleton
+def load_model():
+    model_name = "Abbeite/trail_wl"  # Replace with your actual model name
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Function to generate text with the model
+def generate_text(prompt):
+    formatted_prompt = f"[INST] {prompt} [/INST]"  # Format the prompt according to your specification
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=300)
+    result = pipe(formatted_prompt)
+    return result[0]['generated_text']
+
+st.title("Interact with Your Model")
+
+# User input
+user_input = st.text_area("Enter your prompt:", "")
+
+if st.button("Submit"):
+    if user_input:
+        # Generate text based on the input
+        generated_text = generate_text(user_input)
+        st.write(generated_text)
+    else:
+        st.write("Please enter a prompt.")