Create sematic_evaluation.py

src/evaluation/sematic_evaluation.py

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+# © 2025 Elena Marziali — Code released under Apache 2.0 license.
+# See LICENSE in the repository for details.
+# Removal of this copyright is prohibited.
+
+# Load the model only once
+cross_encoder = CrossEncoder("cross-encoder/nli-deberta-base")
+
+def evaluate_coherence(question, answer):
+    score = cross_encoder.predict([(question, answer)])
+    try:
+        logit = float(score[0]) if isinstance(score[0], (int, float, np.floating)) else float(score[0][0])
+        probability = 1 / (1 + math.exp(-logit))  # Sigmoid function
+        return round(probability, 3)
+    except Exception:
+        return 0.0
+
+# === Scientific reliability score calculation ===
+def calculate_impact_score(citations, h_index, peer_review, publication_year):
+    score = (citations * 0.4) + (h_index * 0.3) + (peer_review * 0.2) - (2025 - publication_year) * 0.1
+    return max(0, score)  # Ensure non-negative
+
+def check_topic_relevance(user_question, extracted_text, threshold=0.7):
+    """Checks whether the topic of the question is consistent with the uploaded file content."""
+    emb_question = embedding_model.encode([user_question])
+    emb_text = embedding_model.encode([extracted_text])
+
+    similarity = np.dot(emb_question, emb_text.T) / (np.linalg.norm(emb_question) * np.linalg.norm(emb_text))
+    return round(similarity, 3), similarity >= threshold
+
+def calculate_response_score(question, answer):
+    score = cross_encoder.predict([(question, answer)])
+    return float(score[0])
+
+def regenerate_if_low_score(question, answer, level, threshold=0.7, iterations=2):
+    evaluation = evaluate_responses_with_ai(question, answer, level)
+    if evaluation["semantic_score"] < threshold:
+        new_question = reformulate_question(question)
+        for i in range(iterations):
+            new_answer = generate_response(new_question, temperature=0.7)
+            new_evaluation = evaluate_responses_with_ai(new_question, new_answer, level)
+            if new_evaluation["semantic_score"] >= threshold:
+                return new_answer
+    return answer
+
+def select_best_version(question, answers):
+    scored = [(r, calculate_response_score(question, r)) for r in answers]
+    scored.sort(key=lambda x: x[1], reverse=True)
+    return scored[0]  # (answer, score)