src/streamlit_app.py

"✅ Model {model_name} loaded successfully on {DEVICE_STR}!")
        return tokenizer, model
        
    except ValueError as e:
        if "Unrecognized configuration class" in str(e):
            progress_placeholder.error(f"❌ Error: {model_name} is not a causal language model suitable for text generation. Please select a different model.")
            st.error(f"Technical details: {str(e)}")
        else:
            progress_placeholder.error(f"❌ Error loading model: {str(e)}")
        raise e
    except Exception as e:
        progress_placeholder.error(f"❌ Unexpected error loading model: {str(e)}")
        raise e

tokenizer, model = load_model(MODEL_NAME)

def generate_text(prompt, max_new_tokens=150, temperature=0.7, top_p=0.9):
    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return text

# ---------- Streamlit UI ----------
st.title(f"Language Model Text Generator ({DEVICE_STR.upper()})")
st.caption("Choose from various pre-trained language models for text generation")

prompt = st.text_area(
    "Enter prompt (English or other supported languages depending on model)",
    value="The future of artificial intelligence is",
    height=150,
)

max_new_tokens = st.slider("Max output tokens", 32, 512, 150)
temperature = st.slider("Temperature", 0.1, 1.2, 0.7)
top_p = st.slider("Top-p (nucleus sampling)", 0.1, 1.0, 0.9)

if st.button("Generate"):
    # Create progress placeholder
    progress_container = st.container()
    
    with progress_container:
        progress_bar = st.progress(0)
        status_text = st.empty()
        
        try:
            status_text.text("🔄 Preparing input...")
            progress_bar.progress(25)
            
            status_text.text("🤖 Generating text... (this may take 20-40s on CPU)")
            progress_bar.progress(50)
            
            output = generate_text(prompt, max_new_tokens, temperature, top_p)
            
            progress_bar.progress(100)
            status_text.text("✅ Generation complete!")
            
            # Clear progress indicators after a short delay
            import time
            time.sleep(1)
            progress_bar.empty()
            status_text.empty()
            
            st.subheader("Model output:")
            st.write(output)
            
        except Exception as e:
            progress_bar.empty()
            status_text.empty()
            st.error(f"❌ Generation failed: {e}")

st.markdown("---")

# Model Status Section
st.subheader("📊 Model Status")
col1, col2, col3 = st.columns(3)

with col1:
    st.metric("Current Model", MODEL_NAME)
with col2:
    st.metric("Device", DEVICE_STR.upper())
with col3:
    # Check if model is loaded by trying to access it
    try:
        model_params = sum(p.numel() for p in model.parameters())
        st.metric("Model Parameters", f"{model_params:,}")
    except:
        st.metric("Model Parameters", "Loading...")

st.markdown("---")
st.markdown(
    """
    **Tips**
    - First run will download model to `~/.cache/huggingface`.
    - DialoGPT models work well for conversational text.
    - GPT-2/DistilGPT-2 work best with English prompts.
    - Use smaller models (DialoGPT-small, DistilGPT-2) for faster CPU response.
    """
)