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already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.12.1->librosa>=0.10.0->llmware) (2.22)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.28.0,>=1.27.53->boto3==1.24.53->llmware) (1.16.0)\n" + ] + } + ], + "source": [ + "!pip install llmware\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import re\n", + "from llmware.prompts import Prompt, HumanInTheLoop\n", + "from llmware.configs import LLMWareConfig\n", + "from llmware.setup import Setup\n", + "from llmware.models import ModelCatalog\n", + "\n", + "class InvoiceProcessor:\n", + " def __init__(self):\n", + " self.prompter = None\n", + "\n", + " def initialize_prompter(self, run_on_cpu=True):\n", + " print(\"Initializing...\")\n", + " try:\n", + " sample_files_path = Setup().load_sample_files(over_write=False)\n", + " invoices_path = os.path.join(sample_files_path, \"Invoices\")\n", + "\n", + " query_list = [\n", + " \"What is the total amount of the invoice?\",\n", + " \"What is the invoice number?\",\n", + " \"What are the names of the two parties?\"\n", + " ]\n", + "\n", + " if run_on_cpu:\n", + " model_name = 'bling-phi-3-gguf'\n", + " else:\n", + " server_uri_string = \"http://11.123.456.789:8088\"\n", + " server_secret_key = \"demo-test\"\n", + " ModelCatalog().setup_custom_llmware_inference_server(server_uri_string, secret_key=server_secret_key)\n", + " model_name = \"llmware-inference-server\"\n", + "\n", + " self.prompter = Prompt().load_model(model_name)\n", + " print(\"Initialization complete.\")\n", + " return True\n", + " except Exception as e:\n", + " print(f\"Initialization failed: {e}\")\n", + " return False\n", + "\n", + " def process_invoices(self):\n", + " if not self.prompter:\n", + " print(\"Prompter is not initialized. Please initialize it first.\")\n", + " return\n", + "\n", + " print(\"Processing invoices...\")\n", + " invoices_path = os.path.join(Setup().load_sample_files(over_write=False), \"Invoices\")\n", + " query_list = [\n", + " \"What is the total amount of the invoice?\",\n", + " \"What is the invoice number?\",\n", + " \"What are the names of the two parties?\"\n", + " ]\n", + "\n", + " total_invoices = sum(1 for _ in os.listdir(invoices_path) if _.endswith(\".pdf\"))\n", + " processed_invoices = 0\n", + "\n", + " for i, invoice in enumerate(os.listdir(invoices_path)):\n", + " if invoice.endswith(\".pdf\"):\n", + " processed_invoices += 1\n", + " progress = f\"{processed_invoices}/{total_invoices}\"\n", + " print(f\"\\nProcessing invoice {progress}: {invoice}\")\n", + "\n", + " for question in query_list:\n", + " source = self.prompter.add_source_document(invoices_path, invoice)\n", + " output = self.prompter.prompt_with_source(question, prompt_name=\"default_with_context\")\n", + "\n", + " for i, response in enumerate(output):\n", + " print(\"LLM Response -\", question, \"-\", re.sub(\"[\\n]\", \" \", response[\"llm_response\"]))\n", + "\n", + " self.prompter.clear_source_materials()\n", + "\n", + " print(\"Invoice processing complete.\")\n", + "\n", + " print(\"All invoices processed.\")\n", + " print(\"Saving prompt state...\")\n", + " self.prompter.save_state()\n", + "\n", + " csv_output = HumanInTheLoop(self.prompter).export_current_interaction_to_csv()\n", + " print(\"CSV output for human review:\", csv_output)\n", + "\n", + " print(\"\\nThank you for using our Invoice Processing System. Your feedback is valuable to us.\")\n", + "\n", + " def run_with_user_interaction(self):\n", + " print(\"Welcome to the Invoice Processing System!\")\n", + " while True:\n", + " print(\"\\nPlease choose an option:\")\n", + " print(\"1. Process invoices\")\n", + " print(\"2. Enter new invoices\")\n", + " print(\"3. Update existing invoices\")\n", + " print(\"4. View processed invoices\")\n", + " print(\"5. Quit\")\n", + "\n", + " choice = input(\"Enter your choice: \")\n", + "\n", + " if choice == \"1\":\n", + " if self.initialize_prompter():\n", + " self.process_invoices()\n", + " elif choice == \"2\":\n", + " self.enter_new_invoices()\n", + " elif choice == \"3\":\n", + " self.update_existing_invoices()\n", + " elif choice == \"4\":\n", + " self.view_processed_invoices()\n", + " elif choice == \"5\":\n", + " print(\"Thank you for using the Invoice Processing System. Goodbye!\")\n", + " break\n", + " else:\n", + " print(\"Invalid choice. Please choose again.\")\n", + "\n", + " def enter_new_invoices(self):\n", + " print(\"Enter details for the new invoice:\")\n", + " total_invoices = int(input(\"Enter the total number of invoices to enter: \"))\n", + " invoices_data = []\n", + " for i in range(total_invoices):\n", + " invoice_data = {}\n", + " print(f\"\\nInvoice {i+1}:\")\n", + " invoice_data[\"total_amount\"] = input(\"Enter the total amount of the invoice: \")\n", + " invoice_data[\"invoice_number\"] = input(\"Enter the invoice number: \")\n", + " invoice_data[\"party_names\"] = input(\"Enter the names of the two parties (separated by comma): \").split(\",\")\n", + " invoices_data.append(invoice_data)\n", + " print(\"\\nNew invoices entered successfully.\")\n", + "\n", + " def update_existing_invoices(self):\n", + " print(\"Feature coming soon!\")\n", + "\n", + " def view_processed_invoices(self):\n", + " csv_file_path = input(\"Enter the path to the CSV file containing processed invoices: \")\n", + " try:\n", + " with open(csv_file_path, \"r\") as file:\n", + " processed_data = file.read()\n", + " print(\"Processed Invoices:\")\n", + " print(processed_data)\n", + " except FileNotFoundError:\n", + " print(\"File not found. Please enter a valid file path.\")\n", + " except Exception as e:\n", + " print(f\"An error occurred: {e}\")\n", + "\n", + "if __name__ == \"__main__\":\n", + " processor = InvoiceProcessor()\n", + " processor.run_with_user_interaction()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944, + "referenced_widgets": [ + "c49f193b63414940bc6012152360b884", + "39d08567df1a42f8b2d72ca48c45d6db", + "8ddf6d895de64216836ddb659c678f8e", + "9ab318597dfa4a6babfc3df323e28fb4", + "8c66717aa7d1451bbc885305ca555cd1", + "a56a8c83e3384e4792537a272150e446", + "6053541ce9e24642a6d3189115914efc", + "a446b3df6ecd45eeb75883747fccc3c2", + "8a6aaf1891194630962c8281b1e46dcc", + "d7576904e7384d10bd4308d3d0ba10dd", + "e0e169556d2d4cdd8f1b849ed5a7d059", + "2f3e982e27af453196e0b2844d4a4db8", + "938a60b096504a07b9a75e0e5e4b3775", + "56b78f825f354d499bd6d81f6f7f09dc", + "49b2b5e9e9ea418fb21d57eeaa820985", + "fe6e3e01403a4612bb68c66dda7b81c8", + "97492bef20034eb4abf4885ac045a6ed", + "a301819dd877442fb5ef52b705a01376", + "90ef503149a840abae6db6a3789bd5cd", + "f40f6e7d93964c7aa71a1b9ab009c6aa", + "80c906b14b464364af660a26b927f880", + "3fa3ed96514b437b85eeafe321a1cca2", + "3a788573108e4083890b254ecf9c977b", + "56eb971b52a0482ebe624b2f9a356ae3", + "170aee03906e43f0b487d4149f0a4c08", + "9819ea85d04046568a615dabb331ac18", + "9e500d7470534cd98b4b5a185f6423e8", + "802ee23747294bd3af5edbf058678d0b", + "24803ea497bf4be3bfbf4e26aca1f1bf", + "ddfa04779f6241ab8b79d5391c7070e2", + "8e296c3ce71f4e53a7a957fe55e7d6d5", + "38c923eafeb9491b89a5aa3ced0e8fe0", + "b280b2f574ae4f9ab5dfc78020eda089", + "1b65cdb54df34ae084dfa4940a5ca852", + "71b5690c504d4d52a408410da62c75fa", + "423ebd266f324cb5835edea0054ae07d", + "d9894ed383954c95a77f285594b1b87d", + "34f8da5d7e0d4948b3a5013e54c54201", + "169decb267e649b1b3e0161d8bce7e8c", + "3f1f7b294f6a453196c11cd3d0131afb", + "cfb28d7ee65a4354bfcb6ff18b99e612", + "b09da7810228486a8917ced821e97822", + "4156fd2e29854661aed5c1603c468d5c", + "0c136835aea84f5dad6f0b1568b65212", + "7bb69d2c26fc443d9af1cf08be5949af", + "895cdfcb0a6c4262a659002a11ac0a53", + "aebb672863eb43d696180785f7cf9b67", + "69784566e11f4c8b9c63ea2c543b617c", + "d9f7ad24c23b4806840c1ee101ebf3f2", + "8f7fe516ca1c4767b3614f36caf17963", + "5f200ffb4e60475a8b5096b24bd4dc71", + "618bbfc0762348e2a49edc027016a8cf", + "3871292ca0f04b9a981f18833f5152de", + "ece71fe55a68452dbf948576e7056147", + "a6aab59578cb4703acdc8ca60e701ad2", + "c03673f42ec64799baeba8f3da49cc8c", + "5ac07f4736de4182a190c274be241b88", + "b42fcb4d6b444924a6790375f3ea0140", + "4a58b6ec1cc7474fa436890b5c0c4dbd", + "b555e71622b6488c9f30c4356a3029eb", + "8eedab4a001d44839b1ea7cce17b0d29", + "c8eef24bb2574a5c9dfc397d94185591", + "9d10ce9d6fd34c1b9fb35a5a5193bc77", + "959ba22159a043159e069e17e7c4d179", + "748c80f0ec25457796ac4ae69c2cfae6", + "83af34f40d0e42ef8333377d38b65c3d", + "f63abdbd837b46fa91dfde4c96332cca", + "0b3c3c389dc34a4d8cd435f051afc051", + "3bdc2b63ae8f4f12883e906745f280dc", + "859e155492b243038927781c552bbc21", + "b26498eccc324f919dd7887cbf478558", + "a8a9096c9898484f884f0ae6856d1303", + "9f7a8d2241f04cb8af8656928a2700d1", + "a5519dfe3d9449d981ee69492fb8bb74", + "253cc7bca6924d4b97241e217371d1c4", + "19a0e28a89d34c22aa8d47f9a0711ea7", + "9487580121b043d08ac069f7010701e9", + "fd07acb9b56c40fb8e0ec49946c77af0", + "c287d2de5f9d44caa6fc5c56831c2d89", + "4baf438ea4bf48d3af8912ae12aae849", + "6ab114b441924f1b829bd6e40cd5127b", + "3a945d7907974d38b78b1b92f08eb770", + "eb2723fae9e54272a1e53d420f9631db", + "70c8fde8f9eb4aa183f6363edf1eda6e", + "1d60a5f5926c435ba8c329c225718d0e", + "9c23fac1a26246158280ea0a77ea5b85", + "7e5ba12d5af042068468941c56aec0ba", + "e808c5ccb4d2463d9ae0d0d4918d3fe6" + ] + }, + "id": "vmtJVZUEgIAx", + "outputId": "2857b50a-e9da-4a4b-c736-a7da83d11fb3" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Welcome to the Invoice Processing System!\n", + "\n", + "Please choose an option:\n", + "1. Process invoices on CPU\n", + "2. Process invoices on GPU/Server\n", + "3. Quit\n", + "Enter your choice: 1\n", + "Initializing...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Fetching 7 files: 0%| | 0/7 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m 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add_prompt_engineering, api_key, inference_dict, get_logits)\u001b[0m\n\u001b[1;32m 5938\u001b[0m \u001b[0mtext_prompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprompt_final\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrailing_space\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5940\u001b[0;31m \u001b[0moutput_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_prompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5942\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput_response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llmware/models.py\u001b[0m in 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reset)\u001b[0m\n\u001b[1;32m 5502\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_past\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_past\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5504\u001b[0;31m \u001b[0mreturn_code\u001b[0m \u001b[0;34m=\u001b[0m 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