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d21e10b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # app.py – 13 kategorideki önerilen modelleri tek ekranda çalıştırır
import gradio as gr
from transformers import pipeline
import torch
# ---------- 1) TEXT GENERATION ----------
gen_pipe = pipeline("text-generation", model="WiroAI/wiroai-turkish-llm-9b", device_map="auto")
# ---------- 2) TEXT CLASSIFICATION ----------
cls_pipe = pipeline("text-classification", model="savasy/bert-base-turkish-sentiment-cased")
# ---------- 3) TOKEN CLASSIFICATION (NER) ----------
ner_pipe = pipeline("token-classification", model="akdeniz27/bert-base-turkish-cased-ner",
aggregation_strategy="simple")
# ---------- 4) FILL-MASK ----------
mask_pipe = pipeline("fill-mask", model="dbmdz/bert-base-turkish-uncased")
# ---------- 5) QUESTION ANSWERING ----------
qa_pipe = pipeline("question-answering", model="emrecan/bert-base-turkish-cased-squad")
# ---------- 6) SUMMARIZATION ----------
summ_pipe = pipeline("summarization", model="mrm8488/bert2bert_shared-turkish-summarization")
# ---------- 7) TRANSLATION ----------
trans_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-trk-en")
# ---------- 8) TEXT2TEXT GENERATION ----------
t2t_pipe = pipeline("text2text-generation", model="google/flan-t5-xl")
# ---------- 9) SENTENCE SIMILARITY ----------
from sentence_transformers import SentenceTransformer, util
sim_model = SentenceTransformer('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
# ---------- 10) AUDIO CLASSIFICATION ----------
# (CPU/GPU uygunluğu için küçük model)
audio_cls_pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-ks")
# ---------- 11) AUTOMATIC SPEECH RECOGNITION (ASR) ----------
asr_pipe = pipeline("automatic-speech-recognition", model="mpoyraz/wav2vec2-xls-r-300m-cv7-turkish")
# ---------- 12) IMAGE-TEXT-TO-TEXT ----------
# Placeholder: Türkçe VLM örneği
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# ---------- 13) ZERO-SHOT IMAGE CLASSIFICATION ----------
zs_pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
# ---------- GRADIO ARAYÜZ ----------
def run_task(task, text=None, context=None, audio=None, image=None, src_lang=None, tgt_lang=None,
sentences=None):
if task == "Text Generation":
return gen_pipe(text, max_new_tokens=256)[0]["generated_text"]
elif task == "Text Classification":
return cls_pipe(text)[0]
elif task == "NER":
return ner_pipe(text)
elif task == "Fill-Mask":
return mask_pipe(text)
elif task == "Question Answering":
return qa_pipe(question=text, context=context)
elif task == "Summarization":
return summ_pipe(text)[0]["summary_text"]
elif task == "Translation":
return trans_pipe(text)[0]["translation_text"]
elif task == "Text2Text":
return t2t_pipe(text)[0]["generated_text"]
elif task == "Sentence Similarity":
emb1 = sim_model.encode(sentences[0])
emb2 = sim_model.encode(sentences[1])
return float(util.cos_sim(emb1, emb2))
elif task == "Audio Classification" and audio is not None:
return audio_cls_pipe(audio)
elif task == "ASR" and audio is not None:
return asr_pipe(audio)["text"]
elif task == "Image Caption" and image is not None:
inputs = processor(image, return_tensors="pt")
out = blip_model.generate(**inputs)
return processor.decode(out[0], skip_special_tokens=True)
elif task == "Zero-Shot Image" and image is not None:
labels = ["cat", "dog", "car", "person"]
return zs_pipe(image, candidate_labels=labels)
else:
return "Lütfen görev için gerekli girdileri sağlayın."
demo = gr.Interface(
fn=run_task,
inputs=[
gr.Dropdown(
["Text Generation", "Text Classification", "NER", "Fill-Mask",
"Question Answering", "Summarization", "Translation",
"Text2Text", "Sentence Similarity", "Audio Classification",
"ASR", "Image Caption", "Zero-Shot Image"],
label="Görev Seç"),
gr.Textbox(placeholder="Metin girin...", label="Metin"),
gr.Textbox(placeholder="Bağlam (QA için)", label="Bağlam", visible=False),
gr.Audio(type="filepath", label="Ses Dosyası"),
gr.Image(type="pil", label="Görsel"),
],
outputs=gr.JSON(label="Sonuç"),
title="Engelsiz-AI",
description="Tüm görevler için tek ekran: Türkçe destekli 13 farklı modeli tek yerde kullanın."
)
if __name__ == "__main__":
demo.launch() |