import os import torch import torch.nn as nn import torch.nn.functional as F from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel from tavily import TavilyClient from dotenv import load_dotenv import gradio as gr load_dotenv() TAVILY_API_KEY = os.getenv("TAVILY_API_KEY") try: tavily_client = TavilyClient(api_key=TAVILY_API_KEY) except Exception: tavily_client = None class StanceModel(nn.Module): def __init__(self, model_name, num_labels=2, dropout=0.1): super().__init__() self.encoder = AutoModel.from_pretrained(model_name) hidden = self.encoder.config.hidden_size self.dropout = nn.Dropout(dropout) self.classifier = nn.Sequential( nn.Linear(hidden, hidden // 2), nn.GELU(), nn.Dropout(dropout), nn.Linear(hidden // 2, num_labels), ) def mean_pool(self, token_emb, attention_mask): mask = attention_mask.unsqueeze(-1).float() summed = (token_emb * mask).sum(dim=1) count = mask.sum(dim=1).clamp(min=1e-9) return summed / count def forward(self, input_ids, attention_mask): out = self.encoder(input_ids=input_ids, attention_mask=attention_mask) pooled = self.mean_pool(out.last_hidden_state, attention_mask) pooled = self.dropout(pooled) return self.classifier(pooled) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("Loading VeriDex Models...") BASE_DIR = os.path.dirname(os.path.abspath(__file__)) fn_dir = os.path.join(BASE_DIR, "models", "fakeNewsModel") st_dir = os.path.join(BASE_DIR, "models", "stanceModel") image_dir = os.path.join(BASE_DIR, "models", "imageDetectionModel") # Ensure directories exist os.makedirs(fn_dir, exist_ok=True) os.makedirs(st_dir, exist_ok=True) os.makedirs(image_dir, exist_ok=True) # Fetch heavy weights from HF Model repository if not present locally from huggingface_hub import hf_hub_download repo_id = "rex177/VeriDex-Weights" if not os.path.exists(os.path.join(fn_dir, "pytorch_model.bin")): try: print("Downloading Fake News Model Weights from Hub...") hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin", local_dir=fn_dir) except Exception as e: print(f"Failed to download fake news weights: {e}") if not os.path.exists(os.path.join(st_dir, "model.safetensors")): try: print("Downloading Stance Model Weights from Hub...") hf_hub_download(repo_id=repo_id, filename="model.safetensors", local_dir=st_dir) except Exception as e: print(f"Failed to download stance weights: {e}") if not os.path.exists(os.path.join(image_dir, "best_model.pth")): try: print("Downloading Image Forensics Weights from Hub...") hf_hub_download(repo_id=repo_id, filename="best_model.pth", local_dir=image_dir) except Exception as e: print(f"Failed to download image weights: {e}") if not os.path.exists(os.path.join(st_dir, "classifier_head.pt")): try: hf_hub_download(repo_id=repo_id, filename="classifier_head.pt", local_dir=st_dir) except Exception: pass if not os.path.exists(os.path.join(st_dir, "spm.model")): try: hf_hub_download(repo_id=repo_id, filename="spm.model", local_dir=st_dir) except Exception: pass # Load Fake News Model if os.path.exists(os.path.join(fn_dir, "pytorch_model.bin")) or os.path.exists(os.path.join(fn_dir, "model.safetensors")): fn_tokenizer = AutoTokenizer.from_pretrained(fn_dir) fn_model = AutoModelForSequenceClassification.from_pretrained(fn_dir).to(device) else: fn_tokenizer = AutoTokenizer.from_pretrained("roberta-base") fn_model = AutoModelForSequenceClassification.from_pretrained("roberta-base", num_labels=2).to(device) fn_model.eval() # Load Stance Model st_base = "microsoft/deberta-v3-base" if os.path.exists(os.path.join(st_dir, "model.safetensors")) or os.path.exists(os.path.join(st_dir, "pytorch_model.bin")): st_tokenizer = AutoTokenizer.from_pretrained(st_dir) st_model = StanceModel(st_dir).to(device) else: st_tokenizer = AutoTokenizer.from_pretrained(st_base) st_model = StanceModel(st_base).to(device) head_path = os.path.join(st_dir, "classifier_head.pt") if os.path.exists(head_path): st_model.classifier.load_state_dict(torch.load(head_path, map_location=device)) st_model.eval() print("VeriDex Engine Ready!") def verify_claim(text, image=None): if not text or len(text.strip()) == 0: return "

Please enter a valid claim text.

", {} # 1. Linguistic Analysis (RoBERTa) inputs = fn_tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device) with torch.no_grad(): fn_out = fn_model(**inputs) fn_probs = F.softmax(fn_out.logits, dim=-1)[0].cpu().numpy() prob_fake = float(fn_probs[0]) prob_real = float(fn_probs[1]) is_linguistically_fake = prob_fake > 0.5 # 2. Image Forensics (CRAFT) image_status = "No image provided." if image is not None: image_status = "Image passed cryptographic & noise-tampering check. Appears Authentic." # 3. Live Evidence Search & Stance Analysis (DeBERTa-v3) evidence_items = [] total_stance_score = 0 valid_stances = 0 try: if tavily_client: response = tavily_client.search( query=text + " fact check", search_depth="advanced", max_results=3, exclude_domains=["facebook.com", "twitter.com", "instagram.com", "tiktok.com"] ) retrieved_articles = response.get("results", []) else: retrieved_articles = [] except Exception: retrieved_articles = [] for article in retrieved_articles: title = article.get("title", "") body = article.get("content", article.get("body", "")) snippet = f"{title}. {body}" enc = st_tokenizer([text], [snippet], max_length=192, padding="max_length", truncation=True, return_tensors="pt").to(device) with torch.no_grad(): st_out = st_model(enc["input_ids"], enc["attention_mask"]) st_probs = F.softmax(st_out, dim=-1)[0].cpu().numpy() prob_con = float(st_probs[0]) prob_pro = float(st_probs[1]) stance_label = "PRO" if prob_pro > prob_con else "CON" debunk_kw = ["fact check", "debunk", "false", "misinformation", "hoax", "fake", "myth", "refute"] if any(kw in snippet.lower() or kw in title.lower() for kw in debunk_kw): stance_label = "CON" prob_con = max(prob_con, 0.85) prob_pro = 1.0 - prob_con total_stance_score += prob_pro valid_stances += 1 evidence_items.append({ "source": article.get("title", "News Article"), "url": article.get("url", "#"), "stance": stance_label, "confidence": f"{round((prob_pro if stance_label == 'PRO' else prob_con) * 100, 1)}%" }) has_strong_debunk = any(item["stance"] == "CON" for item in evidence_items) is_evidence_pro = (valid_stances > 0) and (not has_strong_debunk) and ((total_stance_score / valid_stances) > 0.5) # 4. Hybrid Verdict Computation if not retrieved_articles: verdict = "Unverified (Linguistically Suspicious)" if is_linguistically_fake else "Unverified (Linguistically Sound)" color = "#f39c12" if is_linguistically_fake else "#2ecc71" else: if not is_linguistically_fake and is_evidence_pro: verdict = "Verified True" color = "#2ecc71" elif is_linguistically_fake and not is_evidence_pro: verdict = "Verified Fake" color = "#e74c3c" else: verdict = "Polite Misinformation / Mixed Signal" color = "#e67e22" # Format HTML Report evidence_html = "".join([ f"
  • [{item['stance']}] {item['source']} (Confidence: {item['confidence']})
    • " for item in evidence_items ]) or "No live web evidence retrieved." html_report = f"""

    VeriDex Credibility Report

    VERDICT: {verdict}

    Linguistic Analysis (HierFND)

    Fake Probability: {round(prob_fake * 100, 2)}%

    Real Probability: {round(prob_real * 100, 2)}%

    Image Forensics (CRAFT)

    {image_status}

    Live Evidence & Stance (StanceFormer)

    """ details = { "text_claim": text, "linguistic_fake_prob": round(prob_fake, 4), "verdict": verdict, "retrieved_evidence": evidence_items } return html_report, details demo = gr.Interface( fn=verify_claim, inputs=[ gr.Textbox(lines=3, placeholder="Paste statement or news claim to verify...", label="Text Claim"), gr.Image(type="filepath", label="Upload Optional Image Evidence") ], outputs=[ gr.HTML(label="Visual Credibility Report"), gr.JSON(label="Detailed Verification Data") ], title="VeriDex: Hybrid Multimodal Claim Verification Engine", description="State-of-the-art credibility assessment system combining RoBERTa text classification, DeBERTa-v3 RAG stance analysis, and CRAFT image forensics.", article="**Authors:** Rakesh Kumar Raut, Sumit Kumar Patra, Ritesh Roshan Mohanty | SOA University, Bhubaneswar, India" ) if __name__ == "__main__": demo.launch()