Update app.py

app.py

@@ -1,4 +1,37 @@
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load the base model and the fine-tuned model
+@st.cache_resource
+def load_model():
+    base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
+    model = PeftModel.from_pretrained(base_model, "mohamedyd/Natural-Coder-3B-Instruct-V1")
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Streamlit app
+st.title("Natural-Coder-3B-Instruct-V1 Model Interaction")
+
+# Text input for user prompt
+user_input = st.text_area("Enter your prompt here:", height=150)
+
+# Button to generate response
+if st.button("Generate Response"):
+    if user_input:
+        # Tokenize the input
+        inputs = tokenizer(user_input, return_tensors="pt")
+        
+        # Generate response
+        outputs = model.generate(**inputs, max_length=512, num_return_sequences=1)
+        
+        # Decode the output
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Display the response
+        st.write("Model Response:")
+        st.write(response)
+    else:
+        st.write("Please enter a prompt to generate a response.")