Update README.md

README.md

@@ -13,4 +13,203 @@ base_model:
 pipeline_tag: question-answering
 tags:
 - code
----
+---
+```python
+!pip install flask transformers  pyngrok --quiet   # install library
+
+from flask import Flask, request, jsonify
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import tensorflow
+from pyngrok import ngrok
+import json
+import torch
+import requests
+# format output json
+def parse_questions(raw_json):
+    import re
+    questions = []
+    question_blocks = re.split(r"Q:\s", raw_json["generated_text"])
+    for idx, block in enumerate(question_blocks[1:], start=1):  # Bỏ qua phần đầu trước câu hỏi
+        try:
+            question_match = re.search(r"(.+?)\sA:", block)
+            options_match = re.search(r"A:\s(.+?)\sCorrect:", block, re.DOTALL)
+            correct_match = re.search(r"Correct:\s(.+)", block)
+
+            question = question_match.group(1).strip() if question_match else None
+            options_raw = options_match.group(1).strip() if options_match else None
+            correct_answer = correct_match.group(1).strip() if correct_match else None
+
+
+            options = {}
+            if options_raw:
+                option_list = re.split(r"\d\)", options_raw)
+                for i, option in enumerate(option_list[1:], start=1): 
+                    options[chr(64 + i)] = option.strip()
+
+            questions.append({
+                "id": f"Q{idx}",
+                "Question": question,
+                "options": options,
+                "correct_answer": correct_answer
+            })
+        except Exception as e:
+            print(f"Error parsing block {idx}: {e}")
+
+    return questions
+
+app = Flask(__name__)
+ngrok.set_auth_token("Ngrok_Auth_Token")
+public_url = ngrok.connect(5000)
+print("Ngrok URL:", public_url)
+
+model_name = "TranVanTri352/MCQ_Paragraph_AI_Model"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name, from_tf=True)
+
+
+@app.route('/status', methods=['GET'])
+def model_status():
+    try:
+        # Kiểm tra xem model đã được tải chưa
+        if model and tokenizer:
+            return jsonify({
+                'status': 'ready',
+                'model_name': model_name,
+                'framework': 'transformers',
+                'device': 'cuda' if torch.cuda.is_available() else 'cpu',
+                'message': 'Model is loaded and ready for inference.'
+            }), 200
+        else:
+            return jsonify({
+                'status': 'not_ready',
+                'message': 'Model or tokenizer is not loaded.'
+            }), 500
+    except Exception as e:
+        return jsonify({
+            'status': 'error',
+            'message': f'Error occurred while checking model status: {str(e)}'
+        }), 500
+
+
+
+@app.route('/generate', methods=['POST'])
+def generate_text():
+    try:
+        data = request.json
+        if not data or 'text' not in data:
+            return jsonify({'error': 'Invalid input, "text" is required'}), 400
+
+        input_text = "Generate a question and multiple answers based on this article: " + data['text']
+        inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
+
+        all_outputs = []
+
+        # Loop to generate 5 outputs
+        for i in range(5):
+            torch.manual_seed(i)  # Set different seeds to increase randomness
+            outputs = model.generate(
+                input_ids=inputs["input_ids"],
+                attention_mask=inputs["attention_mask"],
+                max_length=128,
+                do_sample=True,  # Turn on random mode
+                temperature=0.9,  # Increase randomness
+                top_k=30,         # Choose only the word with the highest probability in the top 30
+                top_p=0.9,        # Nucleus sampling
+                repetition_penalty=1.5,  # Limit repetition
+            )
+            output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            all_outputs.append(output_text) 
+
+     
+        final_output = " ".join(all_outputs)
+
+        # Parse the final output into formatted questions
+        parsed_questions = parse_questions({"generated_text": final_output})
+        json_data = json.dumps(parsed_questions)
+        
+       
+
+        return jsonify({'questions': parsed_questions}), 200
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/health', methods=['GET'])
+def health_check():
+    return jsonify({'status': 'Service is healthy'}), 200
+
+print(f"Public URL: {public_url}")
+
+# Flask
+if __name__ == "__main__":
+    app.run(debug=False)
+```
+
+#Test Result
+#Request /generate 
+```json
+{
+    "text": "Originally from Gangseo District, Seoul, Faker was signed by SKT in 2013, and quickly established himself as one of the league's top players. In his debut year, he achieved both an LCK title and a World Championship victory with SKT. From 2014 to 2017, Faker added five more LCK titles to his name, along with two MSI titles in 2016 and 2017, and two additional World Championships in 2015 and 2016. During this time, he also emerged victorious in the All-Star Paris 2014 and the IEM World Championship in 2016. Between 2019 and 2022, Faker secured four more LCK titles, becoming the first player to reach a total of 10. He also represented the South Korean national team at the 2018 Asian Games, earning a silver medal, and the 2022 Asian Games, earning a gold."
+}
+```
+#response body
+```json
+{
+    "questions": [
+        {
+            "Question": "From the passage we know _.",
+            "correct_answer": "Faker will succeed in the LCK title and the MSI titles",
+            "id": "Q1",
+            "options": {
+                "A": "Faker will succeed in the IEM World Championships",
+                "B": "Fakers has won the LCK title and the MSI titles",
+                "C": "Faker is a top player of the SKT league",
+                "D": "Faker became the first player to reach a total of 10"
+            }
+        },
+        {
+            "Question": "Which of the following is true?",
+            "correct_answer": "Faker won three MSI titles from 2016 to 2016.",
+            "id": "Q2",
+            "options": {
+                "A": "Faker won two MSI titles in 2016 and 2017 respectively.",
+                "B": "Fakers won two MSI titles in 2016 and 2016.",
+                "C": "Faker won two MSI titles from 2016 to 2017.",
+                "D": "Faker helped the South Korea national team at the 2018 Asian Games."
+            }
+        },
+        {
+            "Question": "Faker _.",
+            "correct_answer": "was first to win the all-star Paris 2014 and 2016.",
+            "id": "Q3",
+            "options": {
+                "A": "won the All-Star Paris 2014 and IEM World Championship",
+                "B": "was first to win the all-star Paris 2014 and 2016",
+                "C": "appeared on the IEM World Championships in 2016 and 2016",
+                "D": "made his debut at SKT in 2013"
+            }
+        },
+        {
+            "Question": "What did Faker gain from 2014 to 2016 in 2015?",
+            "correct_answer": "Four MS I titles.",
+            "id": "Q4",
+            "options": {
+                "A": "Two MSI titles.",
+                "B": "Five MSI titles.",
+                "C": "Four MSi titles.",
+                "D": "Six MSI titles."
+            }
+        },
+        {
+            "Question": "If you want to win the IEM World Championship, you should go to _.",
+            "correct_answer": "the 2018 Asian Games",
+            "id": "Q5",
+            "options": {
+                "A": "Paris 2014 and 2016",
+                "B": "the IEM World Championship",
+                "C": "the 2018 Asian Games",
+                "D": "the 2012 South Korea national team"
+            }
+        }
+    ]
+}
+```