Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# Load the fine-tuned MFTCoder model
+@st.cache_resource()
+def load_model():
+    MODEL_NAME = "path-to-your-finetuned-model"  # Replace with your MFTCoder fine-tuned model path
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME,
+        torch_dtype=torch.float16,  # Use float16 for performance optimization
+        device_map="auto"  # Automatically allocate to CPU/GPU
+    )
+    return pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Initialize pipeline
+code_generator = load_model()
+
+# Streamlit UI
+st.title("MFTCoder-powered Code Bot 🚀")
+st.subheader("Generate high-quality code snippets with fine-tuned CodeLlama!")
+
+# User input
+prompt = st.text_area("Enter a code prompt to generate code:")
+
+# Generate code
+if st.button("Generate Code"):
+    if prompt.strip():
+        st.info("Generating code... Please wait ⏳")
+        try:
+            # Generate code using the fine-tuned MFTCoder model
+            response = code_generator(
+                prompt,
+                max_length=256,  # Adjust as needed
+                temperature=0.3,  # Lower temperature for accurate outputs
+                num_return_sequences=1,
+                do_sample=True
+            )
+            generated_code = response[0]['generated_text']
+            # Display the code output
+            st.code(generated_code, language="python")  # Default to Python for generated output
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+    else:
+        st.warning("Please enter a prompt.")
+
+st.caption("Created by Shamil")