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import os, requests, tqdm, torch, numpy as np
from torch import nn
from transformers import XLNetModel, XLNetTokenizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# ------------------------------------------------------------------
from huggingface_hub import hf_hub_download
import torch
# Downloads the file automatically from your model repo
MODEL_PATH = hf_hub_download(
repo_id="yeswanthvarma/xlnet-evaluator-model",
filename="xlnet_answer_assessment_model.pt"
)
# Load the model
model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device("cpu")))
def download_model_if_needed():
if os.path.exists(MODEL_PATH):
return
print("▶️ Downloading XLNet weights from Hugging Face …")
with requests.get(HF_URL, stream=True) as r:
r.raise_for_status()
total = int(r.headers.get("content-length", 0))
with open(MODEL_PATH, "wb") as f, tqdm.tqdm(total=total, unit="B", unit_scale=True) as bar:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
bar.update(len(chunk))
print("✅ Download complete.")
download_model_if_needed()
# ------------------------------------------------------------------
xlnet_available = False # will flip to True if load succeeds
class XLNetAnswerAssessmentModel(nn.Module):
def __init__(self):
super().__init__()
self.xlnet = XLNetModel.from_pretrained("xlnet-base-cased")
hidden = 768
self.fc1 = nn.Linear(hidden, 256)
self.fc2 = nn.Linear(256, 64)
self.out = nn.Linear(64, 1)
def forward(self, input_ids, attention_mask=None):
pooled = self.xlnet(input_ids, attention_mask).last_hidden_state.mean(1)
x = torch.relu(self.fc1(pooled))
x = torch.relu(self.fc2(x))
return torch.sigmoid(self.out(x))
try:
tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
model = XLNetAnswerAssessmentModel()
model.load_state_dict(torch.load(MODEL_PATH, map_location="cpu"))
model.eval()
xlnet_available = True
print("✅ Custom XLNet model loaded.")
except Exception as e:
print("⚠️ Could not load XLNet model → fallback to TF‑IDF\n", e)
# ------------------------------------------------------------------
# scoring helpers (unchanged)
# ------------------------------------------------------------------
embedding_cache = {}
def get_model_prediction(q, s, r):
if not xlnet_available:
raise ValueError("XLNet unavailable")
combined = f"{q} [SEP] {s} [SEP] {r}"
inputs = tokenizer(combined, return_tensors="pt", truncation=True, max_length=512, padding=True)
with torch.no_grad():
score = float(model(**inputs).squeeze()) * 100
return round(score)
def tfidf_similarity(t1, t2):
vec = TfidfVectorizer()
mat = vec.fit_transform([t1, t2])
return round(cosine_similarity(mat[0], mat[1])[0][0] * 100)
def fallback_similarity(t1, t2):
w1, w2 = set(t1.lower().split()), set(t2.lower().split())
return round(len(w1 & w2) / len(w1 | w2) * 100) if w1 and w2 else 0
def get_similarity_score(q, s, r):
try:
return get_model_prediction(q, s, r) if xlnet_available else tfidf_similarity(s, r)
except Exception:
return fallback_similarity(s, r) |