auto_review_post.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import PyPDF2\n",
    "import re\n",
    "import openai\n",
    "import csv\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to extract the introduction and discussion sections from a PDF file\n",
    "def extract_sections(pdf_file):\n",
    "    didnt_find = []\n",
    "    # Open the PDF file\n",
    "    with open(pdf_file, 'rb') as f:\n",
    "        pdf_reader = PyPDF2.PdfReader(f)\n",
    "        \n",
    "        # Iterate through the pages of the PDF file and extract the text\n",
    "        text = ''\n",
    "        for i in range(len(pdf_reader.pages)):\n",
    "            page = pdf_reader.pages[i]\n",
    "            text += page.extract_text()\n",
    "\n",
    "        # Convert all the text to lowercase\n",
    "        text = text.lower()\n",
    "\n",
    "        # Use regular expressions to find the introduction and discussion sections\n",
    "        intro_match = re.search(r'introduction', text)\n",
    "        disc_match = re.search(r'discussion', text)\n",
    "\n",
    "        # If the sections are found, extract the first 1500 tokens\n",
    "        if intro_match and disc_match:\n",
    "            intro_end = intro_match.end()\n",
    "            disc_start = disc_match.start()\n",
    "            intro_text = text[intro_end:disc_start]\n",
    "            disc_text = text[disc_match.end():]\n",
    "        \n",
    "            # Take the first 1500 tokens of the text after the title section\n",
    "            intro_tokens = intro_text.split()\n",
    "            disc_tokens = disc_text.split()\n",
    "            if len(intro_tokens) > 1500:\n",
    "                intro_text = ' '.join(intro_tokens[:1500])\n",
    "            if len(disc_tokens) > 1500:\n",
    "                disc_text = ' '.join(disc_tokens[:1500])\n",
    "\n",
    "            return intro_text, disc_text\n",
    "        else:\n",
    "            return 'DidntFind'\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to ask OpenAI the questions and parse the responses\n",
    "def ask_questions(text):\n",
    "    results = []\n",
    "    for question in questions:\n",
    "        message1 = question + \"\\n\" + text\n",
    "        message2 = [{\"role\": \"system\", \"content\": \"Climate change specialist\"}, {\"role\": \"user\", \"content\": f\"{message1}\"}]\n",
    "        response = openai.ChatCompletion.create(\n",
    "            model=\"gpt-3.5-turbo\",\n",
    "            messages= message2,\n",
    "            temperature=0.1\n",
    "        )\n",
    "        answer = response.choices[0].message.content\n",
    "        results.append(answer)\n",
    "        time.sleep(1)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the path to the folder containing the PDF articles\n",
    "pdf_folder = 'path'\n",
    "\n",
    "# Set up OpenAI API credentials\n",
    "openai.api_key = \"Your API Key\"\n",
    "\n",
    "# Set up the questions to ask\n",
    "questions = [\n",
    "    \"What is climate change?\"\n",
    "]\n",
    "\n",
    "# Open the CSV file for writing\n",
    "with open('results.csv', 'w', newline='') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "\n",
    "    # Write the header row\n",
    "    writer.writerow(['File', 'Question', 'Answer'])\n",
    "\n",
    "    # Loop through all the PDF files in the folder\n",
    "    for filename in os.listdir(pdf_folder):\n",
    "        if filename.endswith('.pdf'):\n",
    "            pdf_file = os.path.join(pdf_folder, filename)\n",
    "            print(filename)\n",
    "\n",
    "            # Extract the introduction and discussion sections from the PDF file\n",
    "            sections = extract_sections(pdf_file)\n",
    "            if sections != 'DidntFind':\n",
    "                intro_text, disc_text = sections\n",
    "                print('Extracting the introduction and the discussion')\n",
    "\n",
    "                # Ask OpenAI the questions and parse the responses\n",
    "                intro_results = ask_questions(intro_text)\n",
    "                disc_results = ask_questions(disc_text)\n",
    "                print('Asking ChatGPT')\n",
    "\n",
    "                # Write the results to the CSV file\n",
    "                for i in range(len(questions)):\n",
    "                    print('Saving')\n",
    "                    writer.writerow([filename, questions[i], intro_results[i]])\n",
    "                    writer.writerow([filename, questions[i], disc_results[i]])\n",
    "            \n",
    "            else:\n",
    "                writer.writerow([filename, 'DidntFind', 'DidntFind'])\n",
    "                print('Saving')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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