Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load a tiny, CPU-friendly model for code explanation
+# 'codet5-small' is lightweight and handles multiple languages well
+explainer = pipeline("summarization", model="Salesforce/codet5-small")
+
+# Custom CSS to make it look "Zenith" style
+custom_css = """
+#container { max-width: 500px; margin: auto; }
+.gradio-container { background-color: #0b0f19; color: white; }
+footer { visibility: hidden; }
+"""
+
+def explain_logic(code_input):
+    if not code_input.strip():
+        return "Please paste some code to get started!"
+    
+    # We prefix the input so the model knows it's an explanation task
+    input_text = f"explain: {code_input}"
+    result = explainer(input_text, max_length=100, min_length=30, do_sample=False)
+    return result[0]['summary_text']
+
+# Building the Interface
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="cyan", secondary_hue="slate"), css=custom_css) as demo:
+    gr.Markdown("## ⚡ Zenith Code Explainer")
+    gr.Markdown("Paste your logic below to get a simplified explanation. Optimized for mobile.")
+    
+    with gr.Column(elem_id="container"):
+        code_box = gr.Textbox(
+            label="Input Code", 
+            placeholder="e.g., print([x**2 for x in range(5)])", 
+            lines=8,
+            elem_id="code-input"
+        )
+        submit_btn = gr.Button("Explain Logic", variant="primary")
+        
+        output_text = gr.Textbox(
+            label="Simple Explanation", 
+            interactive=False,
+            lines=5
+        )
+
+    submit_btn.click(fn=explain_logic, inputs=code_box, outputs=output_text)
+
+demo.launch()