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import gradio as gr
import torch
import os
import torchvision
from transformers import pipeline
auth_token = os.environ.get("HUGGING_FACE_HUB_TOKEN")
# Initialize the pipeline
pipe = pipeline(
"text-generation",
model="Jyotiyadav/mistral_7B_NER",
torch_dtype=torch.bfloat16,
device_map="auto",
use_auth_token=auth_token)
# Function to generate output based on input
def generate_output(input_text):
# Prompt for extracting information
prompt = f'''
Your task is to extract the information corresponding to the provided labels from the below given email.
### Labels:
* 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.
For attributes with multiple values, such as measures, volume, weight, package_type, and quantity, provide each value separately in a JSON format.
### Input data:
{input_text}
### Output:
'''
# Generate the result
result = pipe(
f"<s>[INST] {prompt} [/INST]",
do_sample=True,
max_new_tokens=32000,
temperature=0.1,
top_k=0,
top_p=0,
num_return_sequences=1,
)
# Return the generated text
return result[0]['generated_text']
# Create a Gradio interface
iface = gr.Interface(fn=generate_output,
inputs=["text"],
outputs="text",
title="Information Extraction with Mistral-7B",
debug=True,
description="Generate a Information Extraction with OpenLLM.")
iface.launch()
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