Update app.py

app.py

@@ -1,51 +1,38 @@
 import gradio as gr
-import pandas as pd
+import requests
 import re
-import os
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
-model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+API_URL = "https://api-inference.huggingface.co/models/potsawee/t5-large-generation-squad-QuestionAnswer"
+headers = {"Authorization": "Bearer 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"}
 
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+    
 def generate_question_answer_pairs(input_text):
     if input_text is None:
         return "Please enter a text"
 
-    d = {'Question':[],'Answer':[]}
-    df = pd.DataFrame(data=d)
-
     sentences = re.split(r'(?<=[.!?])', input_text)
-    question_answer_pairs = []
+    outputs = [] 
 
     for sentence in sentences:
-        input_ids = tokenizer.encode(sentence, return_tensors="pt")
-        outputs = model.generate(input_ids, max_length=100, num_return_sequences=1)
-        question_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        question_answer_pairs.append(question_answer)
-
-    result = ''
-
-    for question_answer in question_answer_pairs:
-        qa_parts = question_answer.split("?")
-        if len(qa_parts) >= 2:
-            question_part = qa_parts[0] + "?"
-            answer_part = qa_parts[1].strip()
-            new_data = {'Question': [question_part], 'Answer': [answer_part]}
-            df = pd.concat([df, pd.DataFrame(new_data)], ignore_index=True)
-            result += f"Question: {question_part}\nAnswer: {answer_part}\n\n"
+        if sentence.strip():
+            output = query({
+                "inputs": sentence,
+            })
+            outputs.append(output)
             
-    df.to_csv("QAPairs.csv")
-    return result, "QAPairs.csv"
+    return outputs
 
 title = "Question-Answer Pairs Generation"
 input_text = gr.Textbox(lines=4, label="Text:")
-output_file = gr.File(label="Download as csv")
 output_text = gr.Textbox()
 
 interface = gr.Interface(
     fn=generate_question_answer_pairs,
     inputs=input_text,
-    outputs=[output_text, output_file],
+    outputs=output_text,
     title=title,
 )
 interface.launch()