138 lines
6.6 KiB
Plaintext
138 lines
6.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1875b60b-7d4c-4e3a-9751-c9432c395590",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Some weights of the model checkpoint at EleutherAI/gpt-j-6B were not used when initializing GPTJForCausalLM: ['transformer.h.0.attn.bias', 'transformer.h.0.attn.masked_bias', 'transformer.h.1.attn.bias', 'transformer.h.1.attn.masked_bias', 'transformer.h.10.attn.bias', 'transformer.h.10.attn.masked_bias', 'transformer.h.11.attn.bias', 'transformer.h.11.attn.masked_bias', 'transformer.h.12.attn.bias', 'transformer.h.12.attn.masked_bias', 'transformer.h.13.attn.bias', 'transformer.h.13.attn.masked_bias', 'transformer.h.14.attn.bias', 'transformer.h.14.attn.masked_bias', 'transformer.h.15.attn.bias', 'transformer.h.15.attn.masked_bias', 'transformer.h.16.attn.bias', 'transformer.h.16.attn.masked_bias', 'transformer.h.17.attn.bias', 'transformer.h.17.attn.masked_bias', 'transformer.h.18.attn.bias', 'transformer.h.18.attn.masked_bias', 'transformer.h.19.attn.bias', 'transformer.h.19.attn.masked_bias', 'transformer.h.2.attn.bias', 'transformer.h.2.attn.masked_bias', 'transformer.h.20.attn.bias', 'transformer.h.20.attn.masked_bias', 'transformer.h.21.attn.bias', 'transformer.h.21.attn.masked_bias', 'transformer.h.22.attn.bias', 'transformer.h.22.attn.masked_bias', 'transformer.h.23.attn.bias', 'transformer.h.23.attn.masked_bias', 'transformer.h.24.attn.bias', 'transformer.h.24.attn.masked_bias', 'transformer.h.25.attn.bias', 'transformer.h.25.attn.masked_bias', 'transformer.h.26.attn.bias', 'transformer.h.26.attn.masked_bias', 'transformer.h.27.attn.bias', 'transformer.h.27.attn.masked_bias', 'transformer.h.3.attn.bias', 'transformer.h.3.attn.masked_bias', 'transformer.h.4.attn.bias', 'transformer.h.4.attn.masked_bias', 'transformer.h.5.attn.bias', 'transformer.h.5.attn.masked_bias', 'transformer.h.6.attn.bias', 'transformer.h.6.attn.masked_bias', 'transformer.h.7.attn.bias', 'transformer.h.7.attn.masked_bias', 'transformer.h.8.attn.bias', 'transformer.h.8.attn.masked_bias', 'transformer.h.9.attn.bias', 'transformer.h.9.attn.masked_bias']\n",
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"- This IS expected if you are initializing GPTJForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
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"- This IS NOT expected if you are initializing GPTJForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
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]
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},
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Enter the professor's name: Asadpour\n",
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"Enter your research topic: AI\n",
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"Enter your name: Masih\n"
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]
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}
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],
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"source": [
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"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
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"from gtts import gTTS\n",
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"from playsound import playsound\n",
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"import os\n",
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"\n",
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"# Load the tokenizer and model for GPT-J\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
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"model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
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"\n",
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"# Set the padding token to the EOS token\n",
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"tokenizer.pad_token = tokenizer.eos_token\n",
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"\n",
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"def generate_email(professor_name, research_topic, user_name):\n",
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" # Email template\n",
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" prompt = f\"\"\"\n",
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" Dear Professor {professor_name},\n",
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"\n",
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" I am writing to express my interest in pursuing research under your guidance. My research topic revolves around {research_topic}.\n",
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"\n",
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" I believe that your work in this area is groundbreaking, and I am eager to contribute to your ongoing projects.\n",
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"\n",
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" Best regards,\n",
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" {user_name}\n",
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" \"\"\"\n",
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" # Tokenize input\n",
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" inputs = tokenizer(prompt, return_tensors=\"pt\", truncation=True, padding=True)\n",
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" # Generate email with controlled randomness\n",
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" output = model.generate(\n",
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" inputs[\"input_ids\"],\n",
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" attention_mask=inputs[\"attention_mask\"],\n",
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" max_length=len(inputs[\"input_ids\"][0]) + 100,\n",
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" do_sample=True, # Set to True to use temperature and top_p\n",
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" temperature=0.7,\n",
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" top_p=0.9,\n",
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" pad_token_id=tokenizer.eos_token_id\n",
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" )\n",
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" # Decode and return the text\n",
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" generated_email = tokenizer.decode(output[0], skip_special_tokens=True)\n",
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" return generated_email.strip()\n",
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"\n",
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"def text_to_speech(text, output_file=\"email.mp3\", lang='en'):\n",
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" try:\n",
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" if os.path.exists(output_file):\n",
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" os.remove(output_file)\n",
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" tts = gTTS(text, lang=lang)\n",
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" tts.save(output_file)\n",
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" print(f\"Speech saved to {output_file}\")\n",
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" except Exception as e:\n",
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" print(f\"Error generating speech: {e}\")\n",
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"\n",
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"def play_sound(file_path):\n",
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" if not os.path.exists(file_path):\n",
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" print(\"File not found.\")\n",
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" return\n",
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" try:\n",
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" # Using playsound\n",
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" playsound(file_path)\n",
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" print(\"\\nEmail is being read aloud.\")\n",
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" except Exception as e:\n",
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" print(f\"Error playing sound with playsound: {e}\")\n",
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"\n",
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"# Input data\n",
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"professor_name = input(\"Enter the professor's name: \")\n",
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"research_topic = input(\"Enter your research topic: \")\n",
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"user_name = input(\"Enter your name: \")\n",
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"\n",
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"# Generate and print the email\n",
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"email = generate_email(professor_name, research_topic, user_name)\n",
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"print(\"\\nGenerated Email:\\n\")\n",
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"print(email)\n",
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"\n",
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"# Convert the email to speech\n",
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"text_to_speech(email)\n",
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"\n",
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"# Play the generated speech\n",
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"play_sound(\"email.mp3\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89bc527f-4f37-44d3-8454-52a980cf6038",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.19"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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