Update app.py

app.py

@@ -1,11 +1,14 @@
+import os
 import gradio as gr
 from transformers import pipeline
 
-# Fast & lightweight model
+# Read API key from environment
+HF_TOKEN = os.getenv("HF_API_TOKEN")
+
 qa = pipeline(
     "question-answering",
     model="distilbert-base-uncased-distilled-squad",
-    device=-1
+    token=HF_TOKEN
 )
 
 def answer(question, context):
@@ -15,43 +18,30 @@ def answer(question, context):
     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."
+    result = qa(question=question, context=context)
+    answer_text = result.get("answer", "").strip()
 
-        return answer_text
+    if not answer_text:
+        return "❌ Answer not found in the provided content."
 
-    except Exception as e:
-        return f"Error: {str(e)}"
+    return answer_text
 
 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
-        """
-    )
+    gr.Markdown("## ⚡ Free AI Document Chatbot")
 
     context = gr.Textbox(
-        label="📄 Paste document content here",
-        lines=12,
-        placeholder="Paste text from PDF / Excel / Website..."
+        label="📄 Paste document text",
+        lines=10
     )
 
     question = gr.Textbox(
-        label="❓ Ask a question",
-        placeholder="What is this document about?"
+        label="❓ Ask a question"
     )
 
-    output = gr.Textbox(label="✅ Answer", lines=3)
+    output = gr.Textbox(label="✅ Answer")
 
     ask = gr.Button("Ask")
 
-    ask.click(answer, inputs=[question, context], outputs=output)
+    ask.click(answer, [question, context], output)
 
 demo.launch()