Create app.py

app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+try:
+    checkpoint = "Salesforce/codegen-350M-mono"
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+    model = AutoModelForCausalLM.from_pretrained(checkpoint)
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# Function to generate code from description
+def generate_code(description):
+    prompt = f"Generate Python code for the following task: {description}\n"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with st.spinner("Generating code..."):
+        outputs = model.generate(
+            **inputs,
+            max_length=500,
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id  # Avoid padding token warnings
+        )
+    code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract only the code part after the prompt
+    code = code[len(prompt):].strip()
+    return code
+
+# Streamlit UI
+st.title("Code Generation Bot")
+st.write("Enter a description to generate Python code!")
+
+description = st.text_area("Description", placeholder="e.g., Write a function to add two numbers")
+if st.button("Generate Code"):
+    if description.strip():
+        generated_code = generate_code(description)
+        st.code(generated_code, language="python")
+        st.info("Tip: Review the code for accuracy before using!")
+    else:
+        st.warning("Please enter a description!")