upload 5 files

app.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import hashlib
+import joblib
+from collections import Counter
+import gradio as gr
+
+# --- utils (from the notebook) ---
+def ngrams(sentence, n=1, lc=True):
+    ngram_l = []
+    sentence = sentence.lower()
+    for i in range(len(sentence) - n + 1):
+        ngram = sentence[i:i+n]
+        ngram_l.append(ngram)
+    return ngram_l
+
+def all_ngrams(sentence, max_ngram=3, lc=True):
+    all_ngram_list = []
+    for i in range(1, max_ngram + 1):
+        all_ngram_list += [ngrams(sentence, n=i, lc=lc)]
+    return all_ngram_list
+
+MAX_CHARS = 521
+MAX_BIGRAMS = 1031
+MAX_TRIGRAMS = 1031
+MAXES = [MAX_CHARS, MAX_BIGRAMS, MAX_TRIGRAMS]
+
+def reproducible_hash(string):
+    h = hashlib.md5(string.encode("utf-8"), usedforsecurity=False)
+    return int.from_bytes(h.digest()[0:8], 'big', signed=True)
+
+def hash_ngrams(ngrams, modulos):
+    hash_codes = []
+    for ngram_list, modulo in zip(ngrams, modulos):
+        codes = [(reproducible_hash(x) % modulo) for x in ngram_list]
+        hash_codes.append(codes)
+    return hash_codes
+
+def calc_rel_freq(codes):
+    cnt = Counter(codes)
+    total = sum(cnt.values())
+    for k in cnt:
+        cnt[k] /= total
+    return cnt
+
+MAX_SHIFT = []
+for i in range(len(MAXES)):
+    MAX_SHIFT += [sum(MAXES[:i])]
+
+def shift_keys(dicts, MAX_SHIFT):
+    new_dict = {}
+    for i, ngrams_d in enumerate(dicts):
+        for k, v in ngrams_d.items():
+            new_dict[k + MAX_SHIFT[i]] = v
+    return new_dict
+
+def build_freq_dict(sentence, MAXES=MAXES, MAX_SHIFT=MAX_SHIFT):
+    hngrams = hash_ngrams(all_ngrams(sentence), MAXES)
+    fhcodes = map(calc_rel_freq, hngrams)
+    return shift_keys(fhcodes, MAX_SHIFT)
+
+# --- load models ---
+clf = joblib.load("nld.joblib")
+vectorizer = joblib.load("nld_vectorizer.joblib")
+idx2lang = joblib.load("nld_lang_codes.joblib")
+
+input_dim = len(vectorizer.vocabulary_)
+nbr_classes = len(idx2lang)
+
+model = nn.Sequential(
+    nn.Linear(input_dim, 50),
+    nn.ReLU(),
+    nn.Linear(50, nbr_classes)
+)
+model.load_state_dict(torch.load("nld.pth", map_location="cpu"))
+model.eval()
+
+# --- prediction function ---
+def detect_lang(src_sentence):
+    src_sentence = [src_sentence]
+    X_test = vectorizer.transform(map(build_freq_dict, src_sentence))
+    if hasattr(X_test, "toarray"):
+        X_test = X_test.toarray()
+    Y_logits = model(torch.Tensor(X_test))
+    pred_languages = torch.argmax(Y_logits, dim=-1).tolist()
+    return list(map(idx2lang.get, pred_languages))[0]
+
+# --- Gradio UI ---
+with gr.Blocks(title="Antons language detector") as demo:
+    gr.Markdown("# Antons language detector")
+    with gr.Row():
+        with gr.Column():
+            src_sentence = gr.Textbox(
+                label="Text", placeholder="Write your text...")
+        with gr.Column():
+            tgt_sentence = gr.Textbox(
+                label="Language", placeholder="Language will show here...")
+    btn = gr.Button("Guess the language!")
+    btn.click(fn=detect_lang, inputs=[src_sentence], outputs=[tgt_sentence])
+
+demo.launch()