Update app.py

app.py

@@ -1,5 +1,54 @@
+# app.py
+
+import json
+from typing import List
 import gradio as gr
+from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs
+
+# Load test data
+with open("test.json", "r") as read_file:
+    test = json.load(read_file)
+
+# Load train data
+with open("konbert-export-a07a2fb8c3174.json", "r") as json_file:
+    train_data = json.load(json_file)
+
+# Adapt the training data
+adapted_data = []
+for paragraph in train_data:
+    qas_list = []
+    if "answers" in paragraph and "text" in paragraph["answers"] and "answer_start" in paragraph["answers"]:
+        for i in range(len(paragraph["answers"]["text"])):
+            answer_text = paragraph["answers"]["text"][i].strip()
+            if answer_text:
+                qa_dict = {
+                    "id": f"{paragraph['id']}_{i}",
+                    "question": paragraph.get("question", ""),
+                    "answers": [{"text": answer_text, "answer_start": paragraph["answers"]["answer_start"][i]}]
+                }
+                qas_list.append(qa_dict)
+
+        if qas_list:
+            adapted_data.append({
+                "context": paragraph.get("context", ""),
+                "qas": qas_list
+            })
+
+# Model training arguments
+model_args = QuestionAnsweringArgs()
+model_args.train_batch_size = 16
+model_args.evaluate_during_training = True
+model_args.n_best_size = 3
+model_args.num_train_epochs = 5
+
+# Model definition
+model = QuestionAnsweringModel('bert', 'bert-base-uncased', use_cuda=False, args={'overwrite_output_dir': True, 'num_train_epochs': 6})
+model.train_model(adapted_data, num_train_epochs=6)
+model.save_model(f"outputs/bert/final_model")
+
+
 
+# Gradio interface function
 def chatbot(question):
     to_predict = [
         {
@@ -17,6 +66,7 @@ def chatbot(question):
     top_answer = answers[0]['answer'][0]
     return top_answer
 
+# Gradio interface setup
 iface = gr.Interface(
     fn=chatbot,
     inputs="text",
@@ -26,4 +76,5 @@ iface = gr.Interface(
     description="Ask a question about the Normans",
 )
 
+# Launch Gradio interface
 iface.launch()