Update app.py

app.py

@@ -1,4 +1,3 @@
-import time
 import subprocess
 import sys
 
@@ -6,38 +5,26 @@ def install_and_import(package):
     try:
         __import__(package)
     except ImportError:
-        print(f"{package} not installed, installing...")
+        print(f"{package} yüklü değil, kuruluyor...")
         subprocess.check_call([sys.executable, "-m", "pip", "install", package])
 
+# Gerekli paketleri kontrol et ve kur
 install_and_import("gradio")
 install_and_import("transformers")
 install_and_import("torch")
 
+# Şimdi import et
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-MODEL_OPTIONS = {
-    "BrtGPT-124m-Base": "Bertug1911/BrtGPT-124m-Base",
-}
-
-loaded_models = {}
-
-def load_model_and_tokenizer(model_key):
-    if model_key not in loaded_models:
-        print(f"Loading model {model_key}...")
-        model_name = MODEL_OPTIONS[model_key]
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        loaded_models[model_key] = (tokenizer, model)
-    return loaded_models[model_key]
-
-def generate_text(prompt, temperature, top_k, max_new_tokens, model_key):
-    tokenizer, model = load_model_and_tokenizer(model_key)
+# Model ve tokenizer yükleme
+model_name = "Bertug1911/BrtGPT-124m-Base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
+def generate_text(prompt, temperature, top_k, max_new_tokens):
     inputs = tokenizer(prompt, return_tensors="pt")
-
-    start_time = time.time()
     output = model.generate(
         **inputs,
         max_new_tokens=int(max_new_tokens),
@@ -45,37 +32,22 @@ def generate_text(prompt, temperature, top_k, max_new_tokens, model_key):
         top_k=int(top_k),
         do_sample=True,
     )
-    end_time = time.time()
-
     generated_text = tokenizer.decode(output[0], skip_special_tokens=False)
     generated_text = generated_text.replace(" ", "").replace("Ġ", " ")
+    return generated_text
 
-    tokenized_output = tokenizer.tokenize(generated_text)
-    token_count = len(tokenized_output)
-
-    elapsed = end_time - start_time
-    tokens_per_sec = token_count / elapsed if elapsed > 0 else 0
-
-    details = f"Token count: {token_count} | Tokens per second: {tokens_per_sec:.2f}"
-    return generated_text, details
-
-interface = gr.Interface(
+arayuz = gr.Interface(
     fn=generate_text,
     inputs=[
-        gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
+        gr.Textbox(lines=3, placeholder="Your prompt..."),
         gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.01, label="Temperature"),
-        gr.Slider(minimum=1, maximum=50, value=10, step=1, label="Top-K"),
-        gr.Slider(minimum=1, maximum=100, value=15, step=1, label="Max New Tokens"),
-        gr.Dropdown(choices=list(MODEL_OPTIONS.keys()), value="BrtGPT-124m-Base (Smartest and Fastest)", label="Select Model"),
-    ],
-    outputs=[
-        "text",
-        gr.Textbox(label="Performance", interactive=False)
+        gr.Slider(minimum=1, maximum=50, value=1, step=10, label="Top-K"),
+        gr.Slider(minimum=1, maximum=100, value=3, step=15, label="Max New Tokens"),
     ],
+    outputs="text",
     title="BrtGPT-124m-Base",
     description="Adjust the parameters, select the model, and generate text. (0.7 Temp and Top-k = 10 is good for CREATIVITY, 0.1/0.15 Temp. and Top-k = 1-5 is  good for ACCURACY"
 )
 
-interface.launch()
-
+arayuz.launch()