Update app.py

app.py

@@ -1,12 +1,9 @@
 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
-# Change "summarization" to "text2text-generation"
-explainer = pipeline("text2text-generation", model="Salesforce/codet5-small")
+# We use "text-generation" which is confirmed available in your logs
+explainer = pipeline("text-generation", 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; }
@@ -17,11 +14,19 @@ 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']
-
+    # Prefix helps the model understand the context
+    input_text = f"Explain this code: {code_input}"
+    
+    # We set max_new_tokens to control the length of the explanation
+    result = explainer(input_text, max_new_tokens=100, do_sample=False)
+    
+    # text-generation returns a list with 'generated_text'
+    full_text = result[0]['generated_text']
+    
+    # This removes the input prompt from the output so you only see the explanation
+    explanation = full_text.replace(input_text, "").strip()
+    return explanation if explanation else "The model couldn't generate an explanation for this snippet."
+    
 # 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")