May need a gpu

app.py

@@ -1,16 +1,21 @@
 import gradio as gr
 from transformers import pipeline
 
-pipe = pipeline("text-generation", model="gpt2", max_new_tokens=200)
+# Use a model tuned for reasoning and instruction following
+pipe = pipeline(
+    "text-generation",
+    model="microsoft/Phi-3-mini-4k-instruct",
+    device=-1,          # <-- forces CPU
+    max_new_tokens=300
+)
 
 def explain_code(code, level):
-    prompt = f"Explain what the following {level.lower()} code does:\n\n{code}\n\nExplanation:"
+    prompt = f"Explain clearly what the following {level.lower()} code does:\n\n{code}\n\nExplanation:"
     result = pipe(prompt)[0]["generated_text"]
     explanation = result[len(prompt):].strip()
     return explanation
 
 def update_language(level):
-    # Map language names to Gradio code block identifiers
     lang_map = {
         "Python": "python",
         "C": "c",
@@ -19,7 +24,16 @@ def update_language(level):
     }
     return gr.update(language=lang_map.get(level, "text"))
 
-with gr.Blocks(title="💡 Code Explainer") as demo:
+custom_css = """
+div.svelte-1ipelgc, div.ace_content, .ace_editor {
+    cursor: text !important;
+}
+.ace_editor {
+    pointer-events: auto !important;
+}
+"""
+
+with gr.Blocks(css=custom_css, title="💡 Code Explainer") as demo:
     gr.Markdown("### Enter code and get a natural language explanation.")
 
     lang = gr.Radio(["Python", "C", "JavaScript", "Other"], value="Python", label="Language")