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     "text": [
      "Enter the professor's name:  Ali Asadpour\n",
      "Enter your research topic:  AI in architecture\n",
      "Enter your name:  Masih Moafi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Josep\\anaconda3\\envs\\myenv\\lib\\site-packages\\transformers\\generation\\configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Josep\\anaconda3\\envs\\myenv\\lib\\site-packages\\transformers\\generation\\configuration_utils.py:497: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generated Email:\n",
      "\n",
      "Dear Professor Ali Asadpour,\n",
      "\n",
      "    I am writing to express my interest in pursuing research under your guidance. My research topic revolves around AI in architecture.\n",
      "\n",
      "    I believe that your work in this area is groundbreaking, and I am eager to contribute to your ongoing projects.\n",
      "\n",
      "    Best regards,\n",
      "    Masih Moafi\n",
      "    _________________________________________\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "# Load the tokenizer and model for GPT-J\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
    "\n",
    "def generate_email(professor_name, research_topic, user_name):\n",
    "    \"\"\"\n",
    "    Generate a professional and customizable email using GPT-J.\n",
    "    Args:\n",
    "        professor_name (str): The professor's name.\n",
    "        research_topic (str): The user's research topic.\n",
    "        user_name (str): The user's name.\n",
    "    Returns:\n",
    "        str: The generated email text.\n",
    "    \"\"\"\n",
    "    # Email template\n",
    "    prompt = f\"\"\"\n",
    "    Dear Professor {professor_name},\n",
    "\n",
    "    I am writing to express my interest in pursuing research under your guidance. My research topic revolves around {research_topic}.\n",
    "\n",
    "    I believe that your work in this area is groundbreaking, and I am eager to contribute to your ongoing projects.\n",
    "\n",
    "    Best regards,\n",
    "    {user_name}\n",
    "    \"\"\"\n",
    "\n",
    "    # Encode input\n",
    "    input_ids = tokenizer.encode(prompt, return_tensors=\"pt\")\n",
    "\n",
    "    # Generate email with controlled randomness\n",
    "    output = model.generate(\n",
    "        input_ids,\n",
    "        max_length=len(input_ids[0]) + 100,  # Slightly extend length to avoid truncation\n",
    "        do_sample=False,  # Disable sampling for deterministic output\n",
    "        temperature=0.7,  # Control output randomness\n",
    "        top_p=0.9,  # Use nucleus sampling for coherent generation\n",
    "        pad_token_id=tokenizer.eos_token_id  # Prevent padding issues\n",
    "    )\n",
    "\n",
    "    # Decode and return the text\n",
    "    generated_email = tokenizer.decode(output[0], skip_special_tokens=True)\n",
    "    return generated_email.strip()\n",
    "\n",
    "# Input data\n",
    "professor_name = input(\"Enter the professor's name: \")\n",
    "research_topic = input(\"Enter your research topic: \")\n",
    "user_name = input(\"Enter your name: \")\n",
    "\n",
    "# Generate and print the email\n",
    "email = generate_email(professor_name, research_topic, user_name)\n",
    "print(\"\\nGenerated Email:\\n\")\n",
    "print(email)\n"
   ]
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