Update app.py

app.py

@@ -3,53 +3,31 @@ import json
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 
-# 1. Embed model for semantic similarity
-embedder = SentenceTransformer("paraphrase-MiniLM-L3-v2")  # Küçük ve hızlı
+# Semantik model
+embedder = SentenceTransformer("paraphrase-MiniLM-L3-v2")
 
-# 2. LLM model for question generation (TinyLLaMA)
-llm_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-tokenizer = AutoTokenizer.from_pretrained(llm_model_id)
-model = AutoModelForCausalLM.from_pretrained(llm_model_id)
-
-# 3. Load memory-question data
+# Veri yükle
 with open("memory_questions.json", "r") as f:
     memory_data = json.load(f)
 
 memory_texts = [item['description'] for item in memory_data]
 memory_embeddings = embedder.encode(memory_texts)
 
-# 4. En alakalı memory'yi bul + LLM ile soru üret
+# Yalnızca eşleşen soruyu döndür
 def generate_question(user_memory):
-    # (a) En benzer memory'yi bul
     user_embedding = embedder.encode([user_memory])
     similarities = cosine_similarity(user_embedding, memory_embeddings)[0]
     best_match_index = np.argmax(similarities)
-    matched_memory = memory_data[best_match_index]['description']
-
-    # (b) Prompt hazırlığı
-    prompt = f"<|system|>You are a helpful assistant who asks clear, meaningful questions based on short memories.<|user|>Memory: {matched_memory}\nGenerate a question that starts with What, Why, Who, When, or How.<|assistant|>"
-
-    # (c) LLM ile generate et
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-    output = model.generate(input_ids, max_new_tokens=50, do_sample=False)
-    result = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    # (d) Sadece son üretilen kısmı al
-    if "<|assistant|>" in result:
-        result = result.split("<|assistant|>")[-1].strip()
-
-    return result
+    return memory_data[best_match_index]['question']
 
-# 5. Gradio UI
+# Arayüz
 iface = gr.Interface(
     fn=generate_question,
     inputs=gr.Textbox(label="Your Memory"),
-    outputs=gr.Textbox(label="Generated Question"),
-    title="MemoRease – LLM-Enhanced Question Generator",
-    description="Enter a memory. We'll find a similar one and generate a clear, meaningful question using TinyLLaMA."
+    outputs=gr.Textbox(label="Matched Question"),
+    title="MemoRease – Smart Matched Question (No Hallucination)",
+    description="Enter a memory. You'll get the most relevant pre-written question from your dataset."
 )
 
 iface.launch()