Update app.py

app.py

@@ -8,23 +8,27 @@ def load_model():
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
     return tokenizer, model
 
-# Load the model and tokenizer (cached)
-with st.spinner("Loading model..."):
-    tokenizer, model = load_model()
+# Load model
+tokenizer, model = load_model()
 
-# Streamlit UI
-st.title("Code Generator with Hugging Face")
-st.write("Generate code snippets from natural language prompts!")
+st.title("Code Generator")
+st.write("Generate code snippets from natural language prompts using CodeT5!")
 
 prompt = st.text_area("Enter your coding task:", placeholder="Write a Python function to calculate factorial.")
-max_length = st.slider("Select maximum length of generated code:", min_value=20, max_value=200, value=50, step=10)
+max_length = st.slider("Maximum length of generated code:", 20, 300, 100)
 
 if st.button("Generate Code"):
     if prompt.strip():
         with st.spinner("Generating 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)
+            outputs = model.generate(inputs.input_ids, max_length=max_length, num_beams=5, temperature=0.7, early_stopping=True)
+
+            st.write("### Debugging: Raw Model Output")
+            st.json(outputs.tolist())  # Debugging output
+
             generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            st.text_area("Generated Code:", generated_code, height=200)
+            
+            st.write("### Generated Code:")
+            st.code(generated_code, language="python")
     else:
         st.warning("Please enter a prompt!")