Update app.py

app.py

@@ -1,21 +1,25 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 import os
+from huggingface_hub import InferenceClient, HfApi
 
-# Load Hugging Face token from Secrets (Hugging Face Spaces)
+# Load token from Hugging Face Secrets (Hugging Face Spaces handles this)
 HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-MODEL_NAME = "google/flan-t5-base"
 
-# Set up inference client
+# Check if token is valid
+if HF_TOKEN is None:
+    raise ValueError("❌ HUGGINGFACEHUB_API_TOKEN is not set. Go to your Space → Settings → Secrets.")
+
+# Use FLAN-T5 for general QA
+MODEL_NAME = "google/flan-t5-base"
 client = InferenceClient(model=MODEL_NAME, token=HF_TOKEN)
 
-# Define function to generate AI response
+# Define Q&A function
 def ask_ai(question):
     if not question.strip():
-        return "❌ Please enter a question."
-    
+        return "⚠️ Please enter a valid question."
+
     try:
-        prompt = f"Answer this question: {question}"
+        prompt = f"Answer the following question clearly:\n{question}"
         response = client.text_generation(
             prompt=prompt,
             max_new_tokens=100,
@@ -23,20 +27,18 @@ def ask_ai(question):
         )
         return response.strip()
     except Exception as e:
+        # Return the full error message for debugging
         return f"❌ Error: {str(e)}"
 
-# Gradio Interface
-with gr.Blocks(title="AI Code Review & Metadata Validator (Natural Language)") as demo:
+# Gradio UI
+with gr.Blocks(title="AI Code Review & Metadata Validator") as demo:
     gr.Markdown("## 🤖 AI Code Review & Metadata Validator")
     gr.Markdown("Ask any technical question (e.g., Apex, SOQL, Metadata concepts)")
 
-    with gr.Row():
-        question = gr.Textbox(label="Your question", placeholder="e.g. What is a governor limit in Apex?")
+    question = gr.Textbox(label="Your question", placeholder="What is a governor limit in Apex?")
     answer = gr.Textbox(label="AI Response")
 
     ask_btn = gr.Button("Ask")
-
     ask_btn.click(fn=ask_ai, inputs=question, outputs=answer)
 
-# Launch the app
 demo.launch()