Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import random
+
+TASK_MODELS = {
+    "Text Classification": [
+        "savasy/bert-base-turkish-text-classification",
+        "GosamaIKU/bert-topic-classification-turkish",
+        "pamu-tar/bert-turkish-news"
+    ],
+    "Translation": [
+        "Helsinki-NLP/opus-mt-tr-en",
+        "asafaya/kanarya-2b",
+        "boun-tabi-LMG/TURNA"
+    ],
+    "Summarization": [
+        "Ali-Akbar/summarizer-tr",
+        "ozcangundes/mt5-small-turkish-summarization",
+        "RegenAI/umt5-small-turkish-summary"
+    ],
+    "Question Answering": [
+        "savasy/bert-base-turkish-squad",
+        "Rustamshry/Qwen2.5-3B-Self-Instruct-Turkish",
+        "cuneytkaya/fine-tuned-t5-small-turkish-mmlu"
+    ],
+    "Text Generation": [
+        "dbmdz/gpt2-turkish",
+        "WiroAI/wiroai-turkish-llm-9b",
+        "ytu-ce-cosmos/Turkish-Gemma-9b-v0.1"
+    ],
+    "Conversational": [
+        "merttuerk/CyberYapayZeka-V1",
+        "asafaya/kanarya-2b",
+        "mertaydin/phi-turkish"
+    ],
+    "Named Entity Recognition": [
+        "savasy/bert-base-turkish-ner-cased",
+        "akdeniz27/bert-base-turkish-cased-ner",
+        "dbmdz/bert-base-turkish-128k-ner"
+    ],
+    "Sentiment Analysis": [
+        "savasy/bert-base-turkish-sentiment-cased",
+        "pamu-tar/bert-turkish-news",
+        "GosamaIKU/bert-topic-classification-turkish"
+    ],
+    "Sentence Similarity": [
+        "sentence-transformers/LaBSE",
+        "paraphrase-multilingual-mpnet-base-v2",
+        "microsoft/Multilingual-MiniLM-L12-H384"
+    ],
+    "Topic Classification": [
+        "GosamaIKU/bert-topic-classification-turkish",
+        "savasy/bert-base-turkish-text-classification",
+        "pamu-tar/bert-turkish-news"
+    ],
+    "Grammar Correction": [
+        "metatextgmbh/t5-base-grammar-correction",
+        "microsoft/trocr-base-handwritten",
+        "wietsedv/grammar_correction"
+    ],
+    "Intent Detection": [
+        "muratkavlak/bert-base-turkish-uncased-intent-detection",
+        "dbmdz/bert-base-turkish-cased",
+        "savasy/bert-base-turkish-sentiment-cased"
+    ],
+    "Token Classification": [
+        "savasy/bert-base-turkish-ner-cased",
+        "akdeniz27/bert-base-turkish-cased-ner",
+        "dbmdz/bert-base-turkish-128k-ner"
+    ]
+}
+
+def get_random_model(task):
+    models = TASK_MODELS.get(task, None)
+    if not models:
+        return None
+    return random.choice(models)
+
+def run_task(task, text):
+    model_name = get_random_model(task)
+    if model_name is None:
+        return "Bu görev için model bulunamadı."
+
+    if task in ["Text Classification", "Sentiment Analysis", "Topic Classification", "Intent Detection"]:
+        task_name = "text-classification"
+    elif task == "Translation":
+        task_name = "translation"
+    elif task in ["Summarization", "Grammar Correction"]:
+        task_name = "summarization"
+    elif task == "Question Answering":
+        task_name = "question-answering"
+    elif task == "Text Generation":
+        task_name = "text-generation"
+    elif task == "Conversational":
+        task_name = "conversational"
+    elif task in ["Named Entity Recognition", "Token Classification"]:
+        task_name = "token-classification"
+    elif task == "Sentence Similarity":
+        task_name = "feature-extraction"
+    else:
+        task_name = "text-generation"
+
+    try:
+        pipe = pipeline(task_name, model=model_name)
+    except Exception as e:
+        return f"Model yüklenirken hata: {e}"
+
+    try:
+        if task_name == "question-answering":
+            context = text
+            result = pipe(question=text, context=context)
+            return f"📌 Cevap: {result['answer']} (Model: {model_name})"
+        elif task_name == "translation":
+            result = pipe(text)
+            return f"🌐 Çeviri: {result[0]['translation_text']} (Model: {model_name})"
+        elif task_name == "summarization":
+            result = pipe(text)
+            return f"📝 Özet: {result[0]['summary_text']} (Model: {model_name})"
+        elif task_name == "text-classification":
+            result = pipe(text)
+            labels = ", ".join([f"{r['label']} (%{round(r['score']*100, 2)})" for r in result])
+            return f"🧠 Sınıflandırma: {labels} (Model: {model_name})"
+        elif task_name == "token-classification":
+            result = pipe(text)
+            entities = ", ".join([f"{r['entity']}:{r['word']}" for r in result])
+            return f"🏷️ Varlık Tanıma: {entities} (Model: {model_name})"
+        elif task_name == "text-generation":
+            result = pipe(text, max_new_tokens=50)
+            return f"✍️ Üretim: {result[0]['generated_text']} (Model: {model_name})"
+        elif task_name == "conversational":
+            result = pipe(text)
+            return f"💬 Konuşma: {result} (Model: {model_name})"
+        elif task_name == "feature-extraction":
+            vec = pipe(text)
+            return f"🔍 Özellik vektörü uzunluğu: {len(vec[0][0])} (Model: {model_name})"
+        else:
+            return "Bu görev için işlem tanımlı değil."
+    except Exception as e:
+        return f"İşlem sırasında hata: {e}"
+
+def gradio_chat(text, task):
+    return run_task(task, text)
+
+tasks = list(TASK_MODELS.keys())
+
+interface = gr.Interface(
+    fn=gradio_chat,
+    inputs=[gr.Textbox(lines=4, placeholder="Buraya Türkçe metin yazın..."), gr.Dropdown(choices=tasks, label="Görev Seçin")],
+    outputs="text",
+    title="Türkçe Çok Görevli Süper Chatbot",
+    description="Hugging Face üzerindeki en iyi Türkçe modellerden seçmeli çoklu görevli chatbot."
+)
+
+interface.launch()