Update app.py

app.py

@@ -1,17 +1,46 @@
 import gradio as gr
+from transformers import pipeline
 
-# Define the Gradio interface
-iface = gr.Interface(
-    fn=generate_response,
+# **1. Load a better QA model** – using RoBERTa-large for higher accuracy.
+# (You can switch to 'deepset/roberta-base-squad2-distilled' for speed or others as needed.)
+MODEL_NAME = "deepset/roberta-large-squad2"
+qa_pipeline = pipeline(
+    "question-answering", 
+    model=MODEL_NAME, 
+    tokenizer=MODEL_NAME
+    # You can add device=0 here if using a GPU for faster inference
+)
+
+# Define the QA function for Gradio
+def answer_question(question, context):
+    # **2. Use the pipeline with improved parameters**
+    result = qa_pipeline(
+        question=question, 
+        context=context, 
+        handle_impossible_answer=True,  # allow "no answer" if applicable
+        top_k=1,                        # we only want the best answer
+        max_answer_len=30               # increase if expecting longer answers
+    )
+    answer = result.get("answer", "").strip()
+    score = result.get("score", 0.0)
+    # **3. Handle cases where no answer is found or confidence is low**
+    if answer == "" or score < 0.1:
+        # If the model found no answer or is very unsure, return a fallback message
+        return "🤔 I’m not sure – the model couldn’t find a clear answer in the text."
+    return answer
+
+# **4. Set up Gradio interface** with appropriate input/output components
+interface = gr.Interface(
+    fn=answer_question, 
     inputs=[
-        gr.inputs.Textbox(lines=2, label="Input Prompt"),
-        gr.inputs.Slider(minimum=50, maximum=200, step=10, default=100, label="Max Length"),
-        gr.inputs.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.7, label="Temperature")
+        gr.components.Textbox(lines=2, label="Question"), 
+        gr.components.Textbox(lines=10, label="Context")
     ],
-    outputs=gr.outputs.Textbox(label="Generated Response"),
-    title="Llama 2 Chatbot",
-    description="Interact with the Llama 2 model using Gradio."
+    outputs=gr.components.Textbox(label="Answer"),
+    title="Question Answering Demo",
+    description="Ask a question and get an answer from the provided context. " \
+                "Supports unanswerable questions."
 )
 
-# Launch the interface
-iface.launch()
+if __name__ == "__main__":
+    interface.launch()