Initial commit

Jupiter_Group

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+Subproject commit 2dbc58d26610d920a2ce0f2e8b8a31ade6c4751e

app.py

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+import random
+import numpy as np
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    pipeline
+)
+from transformers_interpret import SequenceClassificationExplainer
+import gradio as gr
+
+# ---------------------------
+# Configuration
+# ---------------------------
+SEED = 42
+MODEL_ID = "sosohrabian/my-fine-tuned-bert"  # مدل شما در Model Hub
+
+# ---------------------------
+# Setup Reproducibility
+# ---------------------------
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed_all(SEED)
+
+USE_MPS = torch.backends.mps.is_available()
+device = torch.device("mps" if USE_MPS else "cpu")
+print("Using device:", device)
+
+# ---------------------------
+# Load model and tokenizer from Model Hub
+# ---------------------------
+print("Loading model from:", MODEL_ID)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+
+# ---------------------------
+# Prepare pipeline and explainer
+# ---------------------------
+label_names = {0: "World", 1: "Sports", 2: "Business", 3: "Sci/Tech"}
+
+device_index = 0 if torch.cuda.is_available() else -1
+clf = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None, device=device_index)
+explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)
+
+# ---------------------------
+# Prediction and explanation functions
+# ---------------------------
+def predict(text: str):
+    """Predicts the class probabilities for a given input text."""
+    text = (text or "").strip()
+    if not text:
+        return {}
+    out = clf(text, truncation=True)
+    if isinstance(out, list) and isinstance(out[0], list):
+        out = out[0]
+    results = {}
+    for o in sorted(out, key=lambda x: -x["score"]):
+        idx = int(o["label"].split("_")[1])
+        results[label_names[idx]] = float(o["score"])
+    return results
+
+def explain_html(text: str) -> str:
+    """Generates HTML visualization of important words."""
+    text = (text or "").strip()
+    if not text:
+        return "<i>Enter text to see highlighted words.</i>"
+    atts = explainer(text)
+    toks = [t for t, _ in atts]
+    scores = np.abs([s for _, s in atts])
+    smin, smax = float(np.min(scores)), float(np.max(scores))
+    scores = (scores - smin) / (smax - smin + 1e-8)
+    spans = [
+        f"<span style='background: rgba(255,0,0,{0.15+0.85*s:.2f});"
+        f"padding:2px 3px; margin:1px; border-radius:4px; display:inline-block'>{tok}</span>"
+        for tok, s in zip(toks, scores)
+    ]
+    return "<div style='line-height:2'>" + " ".join(spans) + "</div>"
+
+def predict_and_explain(text: str):
+    """Runs both prediction and explanation."""
+    return predict(text), explain_html(text)
+
+# ---------------------------
+# Gradio App
+# ---------------------------
+demo = gr.Interface(
+    fn=predict_and_explain,
+    inputs=gr.Textbox(lines=3, label="Enter news headline"),
+    outputs=[
+        gr.Label(num_top_classes=4, label="Predicted topic"),
+        gr.HTML(label="Important-word highlights"),
+    ],
+    title="AG News Topic Classifier (Fine-tuned BERT)",
+    description="Classifies news headlines and highlights words that influenced the prediction.",
+    theme="default",
+    examples=[
+        ["Apple unveils new iPhone during annual event"],
+        ["The stock market saw major gains today"],
+        ["Scientists discover new exoplanet"],
+        ["The local team wins the championship"],
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+numpy
+torch
+datasets
+transformers
+accelerate
+scikit-learn
+transformers-interpret
+gradio