Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,79 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-"""
-# Welcome to Streamlit!
+# Set page configuration
+st.set_page_config(
+    page_title="GPT-2 Code Generator",
+    page_icon="💻",
+    layout="wide"
+)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# Title and description
+st.title("💻 GPT-2 Code Generation Model Tester")
+st.markdown(
+    f"Testing model: **[ErikDaska/lr_5e-05](https://huggingface.co/ErikDaska/lr_5e-05)**"
+)
+st.write("Enter a prompt or a partial function definition below to see how your model completes it.")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+# Cache the model and tokenizer loading so it doesn't reload on every button press
+@st.cache_resource
+def load_model():
+    model_name = "ErikDaska/lr_5e-05"
+    with st.spinner("Loading model and tokenizer from Hugging Face... This might take a minute."):
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        
+        # Check if GPU is available
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        model.to(device)
+    return tokenizer, model, device
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+try:
+    tokenizer, model, device = load_model()
+    st.success("Model loaded successfully!", icon="✅")
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+# Sidebar for generation parameters
+st.sidebar.header("Generation Settings")
+max_length = st.sidebar.slider("Max Length", min_value=10, max_value=512, value=128, step=10)
+temperature = st.sidebar.slider("Temperature (Creativity)", min_value=0.1, max_value=1.5, value=0.7, step=0.1)
+top_p = st.sidebar.slider("Top-p (Nucleus Sampling)", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
+do_sample = st.sidebar.checkbox("Use Sampling", value=True)
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+# Main UI text input
+prompt = st.text_area(
+    "Enter Code Prompt:",
+    value="def calculate_factorial(n):\n    # This function calculates the factorial of a number",
+    height=150
+)
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Generation trigger
+if st.button("Generate Code", type="primary"):
+    if not prompt.strip():
+        st.warning("Please enter a prompt first.")
+    else:
+        with st.spinner("Generating code..."):
+            # Encode input
+            inputs = tokenizer(prompt, return_tensors="pt").to(device)
+            
+            # Generate tokens
+            with torch.no_grad():
+                output_sequences = model.generate(
+                    input_ids=inputs["input_ids"],
+                    attention_mask=inputs.get("attention_mask"),
+                    max_length=max_length + len(inputs["input_ids"][0]),
+                    temperature=temperature,
+                    top_p=top_p,
+                    do_sample=do_sample,
+                    pad_token_id=tokenizer.eos_token_id
+                )
+            
+            # Decode output
+            generated_code = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
+            
+            # Display results
+            st.subheader("Generated Output:")
+            st.code(generated_code, language="python")