Delete app.py

app.py

@@ -1,63 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, FSMTForConditionalGeneration, FSMTTokenizer
-from newspaper import Article
-from langdetect import detect
-import torch
-
-# Load models
-classification_model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
-translation_model_name = 'facebook/wmt19-ru-en'
-
-classification_model = AutoModelForSequenceClassification.from_pretrained(classification_model_name)
-classification_tokenizer = AutoTokenizer.from_pretrained(classification_model_name)
-
-translation_model = FSMTForConditionalGeneration.from_pretrained(translation_model_name)
-translation_tokenizer = FSMTTokenizer.from_pretrained(translation_model_name)
-
-def analyze_article(url):
-    try:
-        article = Article(url)
-        article.download()
-        article.parse()
-        text = article.title + '. ' + article.text
-        lang = detect(text)
-    except Exception as e:
-        return f"Error: {e}", "", "", 0, "error"
-
-    translated_text = ""
-    if lang == 'ru':
-        input_ids = translation_tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
-        outputs = translation_model.generate(input_ids)
-        translated_text = translation_tokenizer.decode(outputs[0], skip_special_tokens=True)
-        text = translated_text
-
-    tokens = classification_tokenizer(text, truncation=True, return_tensors="pt")
-    with torch.no_grad():
-        outputs = classification_model(**tokens)
-    score = torch.nn.functional.softmax(outputs.logits[0], dim=0)[1].item()
-    percentage = int(score * 100)
-
-    if percentage > 70:
-        status = "We would not trust this text!"
-    elif percentage > 40:
-        status = "We are not sure about this text!"
-    else:
-        status = "We would trust this text!"
-
-    return text[:1000], lang.upper(), translated_text[:1000] if translated_text else "Not required", percentage, status
-
-demo = gr.Interface(
-    fn=analyze_article,
-    inputs=gr.Textbox(label="Enter Article URL"),
-    outputs=[
-        gr.Textbox(label="Extracted or Translated Text"),
-        gr.Textbox(label="Detected Language"),
-        gr.Textbox(label="Translated Text (if Russian)"),
-        gr.Number(label="Fakeness Score (%)"),
-        gr.Textbox(label="Trust Verdict")
-    ],
-    title="🧠 Misinformation Detection Dashboard",
-    description="Paste a news article URL to detect language, translate if needed, and predict fakeness using a fine-tuned DistilBERT model."
-)
-
-demo.launch()