Delete qamatcher_server.py

qamatcher_server.py

@@ -1,69 +0,0 @@
-
-import os
-from flask import request, jsonify
-from sentence_transformers import SentenceTransformer, util
-
-# Define a writable directory for the model cache
-cache_dir = os.path.join(os.getenv("XDG_CACHE_HOME", "/tmp/.cache"), "huggingface_models")
-os.makedirs(cache_dir, exist_ok=True)
-
-print("Loading SentenceTransformer model (paraphrase-MiniLM-L6-v2)...")
-matcher_model = SentenceTransformer('paraphrase-MiniLM-L6-v2', cache_folder=cache_dir)
-print("SentenceTransformer model loaded.")
-
-# Define a threshold for a "good" match
-SIMILARITY_THRESHOLD = 0.6
-
-def handle_match_question():
-    data = request.get_json()
-    if not data or 'user_question' not in data or 'documents' not in data:
-        return jsonify({'error': 'Invalid request. "user_question" and "documents" are required.'}), 400
-
-    user_question = data['user_question']
-    documents = data['documents']
-
-    if not documents:
-        return jsonify({'answer': "There are no notes to search."})
-
-    # Flatten the list of questions from all documents
-    all_questions = []
-    # Map each question to the original note text
-    question_to_note_map = {}
-
-    for doc in documents:
-        note_text = doc.get('note_text', '')
-        for q in doc.get('questions', []):
-            all_questions.append(q)
-            question_to_note_map[q] = note_text
-    
-    if not all_questions:
-        return jsonify({'answer': "No questions have been generated for your notes yet."})
-
-    try:
-        # Encode the user's question and all stored questions
-        user_embedding = matcher_model.encode(user_question, convert_to_tensor=True)
-        stored_embeddings = matcher_model.encode(all_questions, convert_to_tensor=True)
-
-        # Compute cosine similarity
-        cosine_scores = util.pytorch_cos_sim(user_embedding, stored_embeddings)
-
-        # Find the best match
-        best_match_idx = cosine_scores.argmax()
-        best_score = float(cosine_scores[0][best_match_idx])
-        best_question = all_questions[best_match_idx]
-
-        print(f"User Question: '{user_question}'")
-        print(f"Best matched stored question: '{best_question}' with score: {best_score:.4f}")
-
-        # Check if the match is good enough
-        if best_score > SIMILARITY_THRESHOLD:
-            # Return the note associated with the best-matched question
-            answer = question_to_note_map[best_question]
-        else:
-            answer = "Sorry, I couldn't find a relevant note to answer your question."
-        
-        return jsonify({'answer': answer})
-
-    except Exception as e:
-        print(f"Error during question matching: {e}")
-        return jsonify({'error': str(e)}), 500