Update app.py

app.py

@@ -2,39 +2,43 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
 import json
+import random
 
+# Model yükle
 model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 
-# Örnek veri setini yükle
+# Hafızadan örnekleri yükle
 with open("memory_questions.json", "r") as f:
     memory_data = json.load(f)
 
-# İlk 3-4 örnekten prompt hazırla
-few_shot_examples = "\n".join(
-    [f"Memory: {item['description']}\nQuestion: {item['question']}" for item in memory_data[:5]]
-)
+# Few-shot prompt oluşturan fonksiyon
+def get_few_shot_prompt(memory, k=5):
+    examples = random.sample(memory_data, k)
+    few_shot = "\n".join(
+        [f"Memory: {ex['description']}\nQuestion: {ex['question']}" for ex in examples]
+    )
+    return f"{few_shot}\nMemory: {memory}\nQuestion:"
 
+# Ana fonksiyon
 def generate_question(memory):
-    prompt = f"""{few_shot_examples}
-Memory: {memory}
-Question:"""
-
+    prompt = get_few_shot_prompt(memory)
     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)
 
-    # Çıktıdan yalnızca son soruyu ayrıştır
+    # Sadece cevabı ayıkla
     lines = result.strip().split("Question:")
     return lines[-1].strip() if len(lines) > 1 else result.strip()
 
-# Gradio UI
+# Gradio arayüzü
 iface = gr.Interface(
     fn=generate_question,
     inputs=gr.Textbox(label="Your Memory"),
     outputs=gr.Textbox(label="Generated Question"),
-    title="Memory-Aware Question Generator (TinyLLaMA)"
+    title="MemoRease - TinyLLaMA Chat Generator",
+    description="Write a memory, get a question to help recall more details!"
 )
 
 iface.launch()