Create use.py

use.py

@@ -0,0 +1,131 @@
+import numpy as np
+import torch
+from transformers import BertForQuestionAnswering, BertTokenizerFast
+
+# ── Config ───────────────────────────────────────────────────
+MODEL_DIR  = "model"
+MAX_LENGTH = 384
+DOC_STRIDE = 128
+N_BEST     = 20
+MAX_ANS_LEN = 30
+DEVICE     = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = BertTokenizerFast.from_pretrained(MODEL_DIR)
+model     = BertForQuestionAnswering.from_pretrained(MODEL_DIR).to(DEVICE)
+model.eval()
+print(f"✅ Model loaded on {DEVICE}")
+
+def answer_question(question: str, context: str) -> dict:
+    inputs = tokenizer(
+        question,
+        context,
+        max_length=MAX_LENGTH,
+        truncation="only_second",
+        stride=DOC_STRIDE,
+        return_overflowing_tokens=True,
+        return_offsets_mapping=True,
+        padding="max_length",
+        return_tensors="pt",
+    )
+
+    offset_mapping = inputs.pop("offset_mapping")          # (n_chunks, seq_len, 2)
+    sample_map     = inputs.pop("overflow_to_sample_mapping")
+    sequence_ids   = [inputs.sequence_ids(i) for i in range(len(inputs["input_ids"]))]
+
+    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    start_logits = outputs.start_logits.cpu().numpy()  # (n_chunks, seq_len)
+    end_logits   = outputs.end_logits.cpu().numpy()
+
+    candidates = []
+
+    for chunk_idx in range(len(start_logits)):
+        offsets  = offset_mapping[chunk_idx].numpy()
+        seq_ids  = sequence_ids[chunk_idx]
+
+        s_indexes = np.argsort(start_logits[chunk_idx])[-1:-N_BEST-1:-1]
+        e_indexes = np.argsort(end_logits[chunk_idx])[-1:-N_BEST-1:-1]
+
+        for s in s_indexes:
+            for e in e_indexes:
+                if seq_ids[s] != 1 or seq_ids[e] != 1:
+                    continue
+                if e < s or e - s + 1 > MAX_ANS_LEN:
+                    continue
+                candidates.append({
+                    "score": float(start_logits[chunk_idx][s] + end_logits[chunk_idx][e]),
+                    "text":  context[offsets[s][0]: offsets[e][1]],
+                    "start": int(offsets[s][0]),
+                    "end":   int(offsets[e][1]),
+                })
+
+    if not candidates:
+        return {"answer": "No answer found.", "score": -999, "start": -1, "end": -1}
+
+    best = max(candidates, key=lambda x: x["score"])
+    return {
+        "answer": best["text"],
+        "score":  round(best["score"], 4),
+        "start":  best["start"],
+        "end":    best["end"],
+    }
+
+
+def ask(question: str, context: str):
+    result = answer_question(question, context)
+    print(f"❓ Question: {question}")
+    print(f"💬 Answer  : {result['answer']}")
+    print(f"📊 Score   : {result['score']}")
+    print(f"📍 Position: Char {result['start']}–{result['end']}")
+    print("-" * 60)
+
+
+
+ctx1 = """
+The Amazon rainforest, also known as Amazonia, is a moist broadleaf 
+tropical rainforest in the Amazon biome that covers most of the Amazon 
+basin of South America. This basin encompasses 7,000,000 km² of which 
+5,500,000 km² are covered by the rainforest. The majority of the forest 
+is contained within Brazil, with 60% of the rainforest.
+"""
+ask("How much of the Amazon rainforest is in Brazil?", ctx1)
+
+ctx2 = """
+The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars 
+in Paris, France. It was constructed from 1887 to 1889 as the centerpiece 
+of the 1889 World's Fair. The tower is 330 metres tall and is the tallest 
+structure in Paris.
+"""
+ask("When was the Eiffel Tower built?", ctx2)
+
+ctx3 = """
+Python is a high-level, general-purpose programming language. Its design 
+philosophy emphasizes code readability with the use of significant indentation.
+Python is dynamically typed and garbage-collected. It supports multiple 
+programming paradigms, including structured, object-oriented and functional 
+programming. It was created by Guido van Rossum and first released in 1991.
+Python consistently ranks as one of the most popular programming languages.
+It is widely used in data science, machine learning, web development, and 
+automation. The Python Package Index (PyPI) hosts hundreds of thousands of 
+third-party modules. The standard library is very extensive, offering tools 
+suited to many tasks.
+""" * 3
+
+ask("When was Python first released?", ctx3)
+
+print("\n" + "=" * 60)
+print("🎮 Interactive mode – stop with 'quit'")
+print("=" * 60)
+
+context_interactive = input("📄 Input context:\n> ").strip()
+while True:
+    q = input("\n❓ Question (or type 'quit'): ").strip()
+    if q.lower() == "quit":
+        print("👋 Bye.")
+        break
+    if not q:
+        continue
+    ask(q, context_interactive)