Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the model and tokenizer from Hugging Face
+model_name = "Salesforce/codet5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Streamlit UI
+st.title("Code Generator")
+st.write("Generate code snippets from natural language prompts using CodeT5!")
+
+# Input for natural language prompt
+prompt = st.text_area("Enter your coding task:", placeholder="Write a Python function to calculate the factorial of a number.")
+
+# Slider to control output length
+max_length = st.slider("Maximum length of generated code:", 20, 200, 50)
+
+# Button to trigger code generation
+if st.button("Generate Code"):
+    if prompt.strip():
+        # Tokenize and generate code
+        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
+        outputs = model.generate(inputs.input_ids, max_length=max_length, num_beams=4, early_stopping=True)
+        generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Display generated code
+        st.write("### Generated Code:")
+        st.code(generated_code, language="python")
+    else:
+        st.warning("Please enter a prompt to generate code.")