app.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import Dataset
import gradio as gr
import torch

data = {
    "text": [
        "Proficient in Python and Machine Learning",
        "Excellent written and verbal communication",
        "Experience with cloud platforms like AWS and Azure",
        "Skilled in data visualization and analytics",
        "Project management and Agile methodologies"
    ],
    "label": [0, 1, 0, 0, 1]  # 0 = Technical, 1 = Soft Skill
}

dataset = Dataset.from_dict(data)

model_checkpoint = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

def tokenize(batch):
    return tokenizer(batch["text"], padding=True, truncation=True)

tokenized_dataset = dataset.map(tokenize, batched=True)

model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=2)

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="no",
    per_device_train_batch_size=2,
    num_train_epochs=3,
    logging_steps=10,
    push_to_hub=False,
    report_to="none"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset
)

trainer.train()

def classify(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()
    return "Soft Skill" if prediction == 1 else "Technical Skill"

print(classify("Familiar with cloud computing and Docker"))

interface = gr.Interface(fn=classify, inputs="text", outputs="text")
interface.launch()