Create app.py

app.py

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+from transformers import pipeline
+import torch
+import gradio as gr
+import os
+from PIL import Image
+import pytesseract
+import tempfile
+import shutil
+from pdf2image import convert_from_path
+
+model_name = "deepset/roberta-base-squad2"
+text_qna = pipeline("question-answering", model=model_name, tokenizer=model_name)
+vision_qna = pipeline("document-question-answering", model="impira/layoutlm-document-qa")
+
+# Vision QnA requires: PyTesseract for OCR. Tesseract executable needs to be installed separately.
+# sudo apt install tesseract-ocr (https://tesseract-ocr.github.io/tessdoc/Installation.html)
+
+def load_file(file_input, encoding = 'utf-8'):  
+  if not os.path.exists(file_input):
+    raise FileNotFoundError(f"The file does not exist.")
+
+  with open(file_input, 'r', encoding=encoding) as file:
+    try:
+      content = file.read()
+    except UnicodeDecodeError:
+      # If a UnicodeDecodeError occurs, try reading with 'latin1' encoding
+      with open(file_input, 'r', encoding='latin1') as file:
+        content = file.read()
+
+  return content
+
+def save_image(file):
+    try:
+      temp_dir = tempfile.mkdtemp()
+      file_path = os.path.join(temp_dir, os.path.basename(file.name))
+      # Copy the file from the temporary Gradio directory to our temporary directory
+      shutil.copyfile(file.name, file_path)
+      # when working with saving image files through Gradio, 
+      # using `shutil.copyfile` to handle `NamedString` objects for file uploads is the correct approach
+
+      return file_path
+    except Exception as e:
+      print(e)
+
+
+def save_pdf(file):
+  temp_dir = tempfile.mkdtemp()
+  pdf_path = os.path.join(temp_dir, os.path.basename(file.name))
+  
+  # Copy the file from the temporary Gradio directory to our temporary directory
+  shutil.copyfile(file.name, pdf_path)
+  
+  # Convert PDF to images
+  images = convert_from_path(pdf_path)
+  
+  image_paths = []
+  for i, img in enumerate(images):
+      image_path = os.path.join(temp_dir, f'page_{i}.png')
+      img.save(image_path, 'PNG')
+      image_paths.append(image_path)
+  
+  print(image_paths)
+  return image_paths
+
+
+def qna_text_content(content, question):
+  result = text_qna(question=question, context=content)
+  return result
+
+def qna_image_content(content, question):
+  # result = vision_qna(question=question, image=content)
+  result = vision_qna(content, question)
+  print(f"image question: {question}")
+  return result
+
+def qna_pdf_content(image_paths, question):
+  answers = []
+  try:
+      for image_path in image_paths:
+          result = vision_qna(image=image_path, question=question)
+          print(result[0]['answer'], result[0]['score'])
+          answers.append(result[0]['answer'])
+      return " \n".join(answers)
+  except Exception as e:
+      return f"An error occurred during processing: {e}"
+
+def answer_the_question_for_doc(text_input, file_input, question):
+  #  Order of input parameters is Imp. for Gradio to accept respective Inputs 
+  if file_input is not None:
+    print(f"File type: {type(file_input)}")
+    print(f"File name: {file_input.name}")
+
+    file_extension = file_input.name.split('.')[-1].lower()
+    if file_extension in ['txt']:
+      try:
+        content = load_file(file_input)
+        if not content or not question:
+            return "Please provide both content and a question."
+        result = qna_text_content(content, question)
+        return result["answer"]
+      
+      except FileNotFoundError or Exception as e:
+        print(e)
+        exit(1)
+    
+    elif file_extension in ['png', 'jpeg', 'jpg']:
+      try:
+        img_file_path = save_image(file_input)
+        
+        if not question:
+            return "Please provide a question."
+        result = qna_image_content(img_file_path, question)
+        print(result)
+        return result[0]["answer"]
+
+      except Exception as e:
+        return f"An error occurred during vision processing: {e}" 
+
+    elif file_extension in ['pdf']:
+      try:
+        image_paths = save_pdf(file_input)
+        
+        if not question:
+            return "Please provide a question."
+        result = qna_pdf_content(image_paths, question)
+        print(result)
+        return result
+
+      except Exception as e:
+        return f"An error occurred during vision processing: {e}" 
+
+    else:
+      return "Unsupported file type. Please upload a .txt, ,.pdf, .png, or .jpeg file."
+  else:
+    if not text_input or not question:
+      return "Please provide both content and a question."
+    content = text_input
+    result = qna_text_content(content, question)
+    return result["answer"]
+
+gr.close_all()
+
+with gr.Blocks() as demo:
+    gr.Markdown("# QnA System") 
+    gr.Markdown("This App answers a question based on text content or uploaded file (txt, png, jpeg, pdf).")
+
+    with gr.Row():
+        text_input = gr.Textbox(label="Text Input", placeholder="Enter text content here...")
+        file_input = gr.File(label="File Upload", file_types=['txt', 'png', 'jpeg', 'pdf'])
+
+    question = gr.Textbox(label="Question", placeholder="Enter your question here...")
+    output = gr.Textbox(label="Answer", placeholder="The answer will appear here...")
+
+    text_input.change(lambda x: gr.update(visible=not x), inputs=text_input, outputs=file_input)
+    file_input.change(lambda x: gr.update(visible=not x), inputs=file_input, outputs=text_input)
+
+    button = gr.Button("Get Answer")
+    button.click(answer_the_question_for_doc, inputs=[text_input, file_input, question], 
+                outputs=output)
+demo.launch()
+
+# print(qna_image_content("https://gradientflow.com/wp-content/uploads/2023/10/newsletter87-RAG-simple.png", "What is the step prior to embedding?"))