Update app.py

app.py

@@ -1,7 +1,8 @@
 import gradio as gr
-import requests
+import PyPDF2
 import io
-import json
+import requests
+import torch
 from transformers import AutoTokenizer, AutoModelForQuestionAnswering
 
 # Download and load pre-trained model and tokenizer
@@ -9,34 +10,36 @@ model_name = "distilbert-base-cased-distilled-squad"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 
-def answer_question(pdf_file, question):
-    # Convert PDF to text
-    pdf_data = pdf_file.read()
-    pdf_stream = io.BytesIO(pdf_data)
-    response = requests.post(
-        'https://pdftotext.com/ExtractText',
-        files={'pdffile': pdf_stream},
-        data={'form': 'pdftotext'}
-    )
-    text = response.text.strip()
-
-    # Tokenize question and text
-    input_ids = tokenizer.encode(question, text)
-
-    # Perform question answering
-    outputs = model(torch.tensor([input_ids]), return_dict=True)
-    answer_start = outputs.start_logits.argmax().item()
-    answer_end = outputs.end_logits.argmax().item()
-    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end+1]))
-
-    return answer
+def answer_questions(pdf_file, questions):
+    # Load PDF file and extract text
+    pdf_reader = PyPDF2.PdfFileReader(io.BytesIO(pdf_file.read()))
+    text = ""
+    for i in range(pdf_reader.getNumPages()):
+        page = pdf_reader.getPage(i)
+        text += page.extractText()
+    text = text.strip()
+
+    answers = []
+    for question in questions:
+        # Tokenize question and text
+        input_ids = tokenizer.encode(question, text)
+
+        # Perform question answering
+        outputs = model(torch.tensor([input_ids]), return_dict=True)
+        answer_start = outputs.start_logits.argmax().item()
+        answer_end = outputs.end_logits.argmax().item()
+        answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end+1]))
+        
+        answers.append(answer)
+    
+    return answers
 
 inputs = [
     gr.inputs.File(label="PDF document"),
-    gr.inputs.Textbox(label="Question")
+    gr.inputs.Textbox(label="Questions (one per line)", type="textarea")
 ]
 
-outputs = gr.outputs.Textbox(label="Answer")
+outputs = gr.outputs.Textarea(label="Answers")
 
-gr.Interface(fn=answer_question, inputs=inputs, outputs=outputs, title="PDF Question Answering Tool", 
-             description="Upload a PDF document and ask a question. The app will use a pre-trained model to find the answer.").launch()
+gr.Interface(fn=answer_questions, inputs=inputs, outputs=outputs, title="PDF Question Answering Tool", 
+             description="Upload a PDF document and ask multiple questions. The app will use a pre-trained model to find the answers.").launch()