Create app.py

app.py

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+# -*- coding: utf-8 -*-
+
+import gradio as gr
+from pptx import Presentation
+import io
+from pptx.enum.shapes import MSO_SHAPE_TYPE
+import pandas as pd
+import openai
+import json
+import re
+import os
+# import streamlit as st
+
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+
+
+def get_filename(file_obj):
+    return get_text(file_obj.name)
+
+def get_text(ppt_file):
+
+  msg_fb = os.environ.get("PROMPT")
+
+
+  result = []
+  try:
+    presentation = Presentation(ppt_file)
+
+    for slide_idx, slide in enumerate(presentation.slides): # 슬라이드 마다 읽기
+      string = ''
+
+      # get all text in presentation
+      # It does not distinguish between the title and the content
+      for shape in slide.shapes: # 하나의 슬라이드에 들어간 text 찾기
+        if hasattr(shape, "text"):
+          string += shape.text + "\n"
+
+      # distinguish title and content
+      '''
+      # title
+      title = slide.shapes.title
+      if title is None:
+        string += 'title is None'
+      else:
+        string += title.text
+      string += '\n'
+
+      # content
+      for idx, content in enumerate(slide.shapes.placeholders):
+        # content = slide.shapes.placeholders[1]
+        if idx == 0:
+          continue
+        if content is None:
+          string += 'content is None'
+        else:
+          string += content.text
+      '''
+
+      # table
+      for shape in slide.shapes:
+        if shape.shape_type == MSO_SHAPE_TYPE.TABLE:
+          table_data = []
+
+          row_count = len(shape.table.rows)
+          col_count = len(shape.table.columns)
+          for _r in range(0, row_count):
+            row = []
+            for _c in range(0, col_count):
+              cell = shape.table.cell(_r, _c)
+              # row 별 데이터를 array로 저장
+              row.append(cell.text)
+            # row 데이터를 전체 데이터 저장 array에 저장
+            table_data.append(row)
+
+          # 필요에 따라서는 pandas의 dataframe 등을 이용해서 데이터 저장
+          df_temp = pd.DataFrame(columns=table_data[0], data=table_data[1:])
+          string += str(table_data)
+
+      # add delimiter
+      string += '\n---------------\n'
+      result.append(string)
+
+  except Exception as e:
+    result = f"Error: {str(e)}"
+
+  # make script based on slide title & contents
+  msg_fb = msg_fb + "- Input:\n" + ' '.join(result)
+
+  response = openai.ChatCompletion.create(
+  model="gpt-3.5-turbo",
+  messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": msg_fb},
+    ]
+  )
+  response = response.choices[0].message.content
+
+  done_presentation = preprocessing_script(response, presentation)
+
+  # save pptx file
+  done_presentation.save('edited_ppt_file.pptx')
+
+  # return ' '.join(result), 'edited_ppt_file.pptx'
+  return response, 'edited_ppt_file.pptx'
+
+def preprocessing_script(response, presentation):
+  # split chatgpt's response
+  pattern = r"- Slide (\d+) Script:"
+  segments = re.split(pattern, response)
+
+  # print(response)
+
+  for i in range(1, len(segments), 2): # 몇번째 슬라이드 인지도 나와서 2칸씩 점프
+    slide_num = int(segments[i])
+    script = segments[i+1].strip()
+
+    print(script)
+
+    slide = presentation.slides[slide_num - 1]
+
+    # add sclide note each of them
+    notes_slide = slide.notes_slide
+    text_frame = notes_slide.notes_text_frame
+    text_frame.text = script
+
+  return presentation
+
+
+# with gr.Blocks() as demo:
+#   upload_button = gr.UploadButton("Click to Upload a File", file_types=[".pptx"], file_count="multiple")
+#   upload_button.upload(get_filename, upload_button, "text")
+
+  # output = gr.Textbox(label='output_box')
+
+
+demo = gr.Interface(get_filename, "file", outputs=["text", "file"])
+
+demo.launch()
+