| import streamlit as st |
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import pandas as pd |
| import os |
|
|
| |
| st.set_page_config( |
| page_title="QDF Classifier", |
| page_icon="🔍", |
| layout="wide", |
| initial_sidebar_state="collapsed", |
| menu_items=None |
| ) |
|
|
| MODEL_ID = "dejanseo/QDF" |
| HF_TOKEN = os.getenv("HF_TOKEN") |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| |
| model = AutoModelForSequenceClassification.from_pretrained( |
| MODEL_ID, |
| token=HF_TOKEN, |
| low_cpu_mem_usage=False, |
| device_map=None |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN) |
| model.to(device) |
|
|
| model.eval() |
|
|
| def classify(prompt: str): |
| inputs = tokenizer( |
| prompt, |
| return_tensors="pt", |
| truncation=True, |
| padding=True, |
| max_length=512 |
| ).to(device) |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| probs = torch.softmax(logits, dim=-1).squeeze().cpu() |
| pred = torch.argmax(probs).item() |
| confidence = probs[pred].item() |
| return pred, confidence |
|
|
| |
| st.markdown(""" |
| <style> |
| @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap'); |
| |
| html, body, div, span, input, label, textarea, button, h1, h2, p, table { |
| font-family: 'Montserrat', sans-serif !important; |
| } |
| |
| [class^="css-"], [class*=" css-"] { |
| font-family: 'Montserrat', sans-serif !important; |
| } |
| |
| header {visibility: hidden;} |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| |
| st.title("QDF Classifier") |
| st.write("Built by [**Dejan AI**](https://dejan.ai)") |
| st.write("This classifier determines whether query deserves freshness.") |
|
|
| |
| example_text = """how would a cat describe a dog |
| how to reset a Nest thermostat |
| write a poem about time |
| is there a train strike in London today |
| summarize the theory of relativity |
| who won the champions league last year |
| explain quantum computing to a child |
| weather in tokyo tomorrow |
| generate a social media post for Earth Day |
| what is the latest iPhone model""" |
|
|
| user_input = st.text_area( |
| "Enter one search query per line:", |
| placeholder=example_text |
| ) |
|
|
| if st.button("Classify"): |
| raw_input = user_input.strip() |
| if raw_input: |
| prompts = [line.strip() for line in raw_input.split("\n") if line.strip()] |
| else: |
| prompts = [line.strip() for line in example_text.split("\n")] |
|
|
| if not prompts: |
| st.warning("Please enter at least one prompt.") |
| else: |
| info_box = st.info("Processing... results will appear below one by one.") |
| table_placeholder = st.empty() |
| results = [] |
|
|
| for p in prompts: |
| with st.spinner(f"Classifying: {p[:50]}..."): |
| label, conf = classify(p) |
| results.append({ |
| "Prompt": p, |
| "QDF": "Yes" if label == 1 else "No", |
| "Confidence": round(conf, 4) |
| }) |
| df = pd.DataFrame(results) |
| table_placeholder.data_editor( |
| df, |
| column_config={ |
| "Confidence": st.column_config.ProgressColumn( |
| label="Confidence", |
| min_value=0.0, |
| max_value=1.0, |
| format="%.4f" |
| ) |
| }, |
| hide_index=True, |
| ) |
| info_box.empty() |
|
|
| |
| st.subheader("Working together.") |
| st.write("[**Schedule a call**](https://dejan.ai/call/) to see how we can help you.") |
|
|
