Update app.py

app.py

@@ -1,29 +1,78 @@
 import gradio as gr
 from transformers import pipeline
+from transformers.pipelines import PipelineException
 
-# Load the Question Answering pipeline
-qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+# Preload models
+models = {
+    "distilbert-base-uncased-distilled-squad": "distilbert-base-uncased-distilled-squad",
+    "roberta-base-squad2": "deepset/roberta-base-squad2",
+    "bert-large-uncased-whole-word-masking-finetuned-squad": "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "albert-base-v2": "twmkn9/albert-base-v2-squad2",
+    "xlm-roberta-large-squad2": "deepset/xlm-roberta-large-squad2"
+}
 
-def answer_question(context, question):
-    result = qa_model(question=question, context=context)
+loaded_models = {}
+
+def load_model(model_name):
+    if model_name not in loaded_models:
+        try:
+            loaded_models[model_name] = pipeline("question-answering", model=models[model_name])
+        except PipelineException as e:
+            return str(e)
+    return loaded_models[model_name]
+
+def answer_question(model_name, file, question):
+    model = load_model(model_name)
+    if isinstance(model, str):
+        return model, "", ""
+    
+    context = file.read() if file else ""
+    result = model(question=question, context=context)
     answer = result['answer']
     score = result['score']
-    return answer, f"{score:.2f}"
+    
+    # Explain score
+    score_explanation = f"The confidence score ranges from 0 to 1, where a higher score indicates higher confidence in the answer's correctness. In this case, the score is {score:.2f}. A score closer to 1 implies the model is very confident about the answer."
+    
+    return answer, f"{score:.2f}", score_explanation
 
 # Define the Gradio interface
-interface = gr.Interface(
-    fn=answer_question,
-    inputs=[
-        gr.Textbox(lines=10, placeholder="Enter the context here...", label="Context"), 
-        gr.Textbox(lines=2, placeholder="Enter your question here...", label="Question")
-    ],
-    outputs=[
-        gr.Textbox(label="Answer"), 
-        gr.Textbox(label="Confidence Score")
-    ],
-    title="Question Answering System",
-    description="Upload a document and ask questions to get answers based on the context."
-)
+with gr.Blocks() as interface:
+    gr.Markdown(
+        """
+        # Question Answering System
+        Upload a document and ask questions to get answers based on the context.
+        """)
+    
+    with gr.Row():
+        model_dropdown = gr.Dropdown(
+            choices=list(models.keys()),
+            label="Select Model",
+            value="distilbert-base-uncased-distilled-squad"
+        )
+    
+    with gr.Row():
+        file_input = gr.File(label="Upload Document")
+        question_input = gr.Textbox(lines=2, placeholder="Enter your question here...", label="Question")
+    
+    with gr.Row():
+        answer_output = gr.Textbox(label="Answer")
+        score_output = gr.Textbox(label="Confidence Score")
+        explanation_output = gr.Textbox(label="Score Explanation")
+    
+    with gr.Row():
+        submit_button = gr.Button("Submit")
+    
+    gr.Markdown("### Progress")
+    with gr.Row():
+        progress_bar = gr.Progress(label="Loading Model...")
+
+    submit_button.click(
+        answer_question,
+        inputs=[model_dropdown, file_input, question_input],
+        outputs=[answer_output, score_output, explanation_output],
+        show_progress=progress_bar,
+    )
 
 if __name__ == "__main__":
     interface.launch()