Update app.py

app.py

@@ -1,23 +1,57 @@
 import gradio as gr
 from transformers import pipeline
 
-qa = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+# Fast & lightweight model
+qa = pipeline(
+    "question-answering",
+    model="distilbert-base-uncased-distilled-squad",
+    device=-1
+)
 
 def answer(question, context):
     if not context.strip():
-        return "Please upload or paste content first."
-    return qa(question=question, context=context)["answer"]
+        return "⚠️ Please paste document content first."
 
-with gr.Blocks() as demo:
-    gr.Markdown("## Free AI Document Chatbot")
+    if not question.strip():
+        return "⚠️ Please enter a question."
+
+    try:
+        result = qa(question=question, context=context)
+        answer_text = result.get("answer", "").strip()
+
+        if not answer_text:
+            return "❌ Answer not found in the provided content."
 
-    context = gr.Textbox(label="Paste document text here", lines=10)
-    question = gr.Textbox(label="Ask a question")
+        return answer_text
 
-    output = gr.Textbox(label="Answer")
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        ## ⚡ Super-Fast Free AI Document Chatbot
+        **How to use:**
+        1. Paste document text below
+        2. Ask a question based ONLY on that content
+        """
+    )
+
+    context = gr.Textbox(
+        label="📄 Paste document content here",
+        lines=12,
+        placeholder="Paste text from PDF / Excel / Website..."
+    )
+
+    question = gr.Textbox(
+        label="❓ Ask a question",
+        placeholder="What is this document about?"
+    )
+
+    output = gr.Textbox(label="✅ Answer", lines=3)
 
     ask = gr.Button("Ask")
 
-    ask.click(answer, [question, context], output)
+    ask.click(answer, inputs=[question, context], outputs=output)
 
 demo.launch()