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| import gradio as gr | |
| import torch | |
| import re | |
| import os | |
| auth_token = os.environ.get("HUGGING_FACE_HUB_TOKEN") | |
| from unsloth import FastLanguageModel | |
| instruction = """ | |
| From the given email, extract the following key values. The keys are explained below: | |
| * pickup_location: Street address of the origin location of goods. | |
| * pickup_cap: Postal code or ZIP code of the pickup location. | |
| * pickup_port: Port of pickup, often used in international shipping. | |
| * pickup_state: Only Country of pickup location. | |
| * delivery_location: Street address of the destination location of goods. | |
| * delivery_cap: Postal code or ZIP code of delivery location. | |
| * delivery_port: Port of delivery, similar to pickup port. | |
| * delivery_state: State or region of delivery location. | |
| * total_quantity: Overall quantity of shipped items (e.g., pieces, boxes). Calculate the total_quantity by summing the quantity of all packages. | |
| * total_weight: Total weight of the shipment (e.g., kg, lbs). Calculate the total_weight by summing the weights of all packages. | |
| * total_volume: Total volume of the shipment (e.g., cubic meters, cubic feet). Calculate the total_volume by summing the volumes of all packages. | |
| * quantity: Individual Quantity of a specific item being shipped. | |
| * package_type: Individual Type of packaging used (e.g., pallets, cartons). | |
| * weight: Individual Weight of a specific package. | |
| * measures: Individual Dimensions or measurements of a package. | |
| * stackable: Indicates whether the shipment is stackable (True or False). | |
| * volume: Individual Volume of a specific package. | |
| * commodity: Type of goods or commodities being shipped. | |
| * company: Name of the email sending company, also the shipping company or carrier. | |
| * incoterms: Choose available options: EXW, FCA, FAS, FOB, CFR, CIF, CPT, CIP, DAP, DPU, DDP. | |
| """ | |
| def process_output(output): | |
| """ | |
| Process the output to extract the response. | |
| """ | |
| # Define the regex pattern | |
| pattern = r'### Response:\n?(.*?)<\|endoftext\|>' | |
| # Search for the pattern in the output | |
| match = re.search(pattern, output, re.DOTALL) | |
| if match: | |
| # Extract the response | |
| response = match.group(1) | |
| # Remove specified symbols | |
| cleaned_str = re.sub(r'\\n|\\\\|\\\'', '', response) | |
| return cleaned_str | |
| else: | |
| return output | |
| # Define the function for generating output based on input | |
| def generate_output(input_text,model): | |
| # Prompt for the instruction | |
| output = "" | |
| # Initialize the FastLanguageModel | |
| model, tokenizer = FastLanguageModel.from_pretrained( | |
| model_name = "DataIntelligenceTeam/NER-Phi-3-mini-4k-instruct", | |
| max_seq_length = 2500, | |
| dtype = None, | |
| load_in_4bit = True, | |
| ) | |
| FastLanguageModel.for_inference(model) # Enable native 2x faster inference | |
| alpaca_prompt = f""" | |
| Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. | |
| ### Instruction: | |
| {instruction} | |
| ### Input: | |
| {input_text} | |
| ### Response: | |
| """ | |
| # Tokenize the input text | |
| inputs = tokenizer([alpaca_prompt], return_tensors="pt").to("cuda") | |
| # Generate outputs | |
| outputs = model.generate(**inputs, max_new_tokens=2500, use_cache=True) | |
| output = tokenizer.batch_decode(outputs) | |
| cleaned_response = process_output(str(output)) | |
| return output,cleaned_response | |
| examples = [ | |
| ["""dubai", "Dear Team, ; Please quote SEA option for below inquiry ; POD :Jebel Ali . ; estimated packing details ; 3300x950x1350 mm ; 3800x1700x1350 mm ; 1900x2000x1200 mm ; 1900x2000x1200 mm ; 2600x1300x1200 mm ; 3900x1100x1350 mm ; 820x780x950 mm ; Total weight kg 5000 approx ; Not stackable ; Marrone Srl ; via Rui, 5 - Loc. Prà dei Risi ; I - 33080 Zoppola (PN) ; Warehouse: 08:00 – 12:00 / 13:00 – 16:30 ; Warehouse Handling + Free days w.e.f 01-01-2024 ; AED 250 + vat until 10 CBM - AED 350 + Vat 11 CBM on wards ; Free time applicable - 7 days only for General cargo from DO readiness & No free time for HAZ CARGO, post which standard storage tariff will apply. ; Important Notice ; : Considering the ongoing circumstances, please be informed after booking confirmation the rates and schedules may undergo changes without advance notice. ; Please also expect vessel delays and changes in transit time. ; CSS will not be liable for any claims that arise due to this. ; Deepak Unnikrishnan ; Sr Sales Coordinator ; Consolidated Shipping Service L.L.C. ; Office: +971 4 883 1303 | Ext: 1163| Toll Free: 800277 | Mobile: +971501513697| ; Dubai | United Arab Emirates ; nvo-import5@cssdubai.com | www.cssgroupsite.com ; Our Offices: Dubai | Abu Dhabi | Sharjah | Ras-Al-Khaimah | Bahrain | Oman | Qatar | Saudi Arabia | Kuwait | Iraq | Africa | Turkiye | India | Sri Lanka | ; Disclaimer: This email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please delete all copies and notify the sender immediately. ; Please read our ; disclaimer."""], | |
| ["""I: CIC/2324/806 // Rates Ex Italy To Chennai Port", "Dear Ceriana , ; Please quote your best rate Ex-Works Cesena Italy, By Sea to Chennai Sea Port for below given details : ; Cargo is non stackable and non tiltable, send quote accordingly. ; ITEM - EQUIPMENT FOR PHYSICAL EXERCISE ; Total - 10 Packages ; Net Weight - 1838.62 Kgs ; Total Weight - 2291.85 Kgs ; Total Volume (m3) - 17.36 ; Refer attached Packing List for Dimensions, etc. ; Pick-Up Address : ; Technogym S.P.A ; Via Calcinaro, 2861 ; 47521 Cesena ( FC) ; Italy ; Thanks & Regards, ; RAKSHANA.R ; A Group of RADAR VENTURES PVT LTD. ; 6th Floor, Menon Eternity, St. Marys Road, ; Alwarpet, Chennai-600018 ; PH NO:7305888727 ; MAIL ID : ; rakshana.r@magikcargo.com"""], | |
| ["""I: KEELUNG", "Ciao Paolo, ; Per favore mi quoti per: ; Winery: Cantina Tollo ; pick up::66010 ; POD: Keelung ; Cargo size : 120x 100x190 cm/ 2 Pallets 1581/Kg ; 210 cases ,1581kg, 2Pallets about 4.56 CBM ; Grazie ; Cordiali saluti/ Best regards ; ________________________________________________________________________________ ; Franco Raiola – Area manager ; Cargo Compass spa ; Via Lavoria 56/L/M/N | Cenaia | Crespina Lorenzana | Pisa | 56040 | ITALY ; fraiola@cargocompass.it | O +39.050.643133 | F +39.050.644687 | C+39.335.1277205 | www.cargocompassworld.com"""]] | |
| model_options = ["DataIntelligenceTeam/NER-Phi-3-mini-4k-instruct"] | |
| #,"DataIntelligenceTeam/NER-gemma-7b-bnb-4bit","DataIntelligenceTeam/llama-3-8b-Instruct-bnb-4bit","DataIntelligenceTeam/mistral-7b-instruct-v0.2-bnb-4bit"] | |
| # Create Gradio interface | |
| iface = gr.Interface(fn=generate_output, | |
| inputs=[ | |
| gr.inputs.Textbox(label="Input Text"), | |
| #gr.inputs.Dropdown(label="Select the Fine-tuned Model", choices=["DataIntelligenceTeam/NER-Phi-3-mini-4k-instruct","DataIntelligenceTeam/llama-3-8b-Instruct-bnb-4bit"]) | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label="Original Output Text"), | |
| gr.outputs.Textbox(label="Formatted JSON") ], | |
| examples=examples, | |
| title="Email Information Extraction", | |
| description="Extract key information from the provided email.") | |
| iface.launch() |