Create app.py

app.py

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+# app.py
+import gradio as gr
+import yake
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import math
+
+# --- Models / tools (küçük ve CPU-dostu)
+MODEL_NAME = "google/flan-t5-small"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+# YAKE keyword extractor (çok hafif)
+def extract_keywords(text, lang="en", max_kw=8):
+    kw_extractor = yake.KeywordExtractor(lan=lang, n=1, top=max_kw)
+    kws = kw_extractor.extract_keywords(text)
+    # returns list of keywords (sorted)
+    return [kw for kw, score in kws]
+
+# Basit SEO puanı hesaplama
+def seo_score(title, description, keywords, tags):
+    score = 0
+    # title presence & length
+    if title and title.strip():
+        score += 15
+        L = len(title)
+        if 40 <= L <= 70:
+            score += 20
+        elif 30 <= L < 40 or 71 <= L <= 90:
+            score += 10
+    # keywords in title/description
+    kw_in_title = sum(1 for k in keywords if k.lower() in (title or "").lower())
+    kw_in_desc = sum(1 for k in keywords if k.lower() in (description or "").lower())
+    score += min(20, kw_in_title * 10)
+    score += min(15, kw_in_desc * 5)
+    # description length
+    dlen = len(description or "")
+    if dlen >= 300:
+        score += 20
+    elif dlen >= 150:
+        score += 10
+    # tags
+    if tags:
+        if 3 <= len(tags) <= 15:
+            score += 10
+        elif len(tags) > 15:
+            score += 5
+    # normalize
+    return min(100, score)
+
+# Title & description generator (Flan-T5)
+def gen_suggestions(main_text, keywords, max_titles=3):
+    prompt = (
+        "You are an assistant that generates catchy YouTube video titles and an SEO-optimized description.\n"
+        f"Main content: {main_text}\n"
+        f"Keywords: {', '.join(keywords)}\n"
+        "Produce 3 short catchy titles (each <70 chars) and one SEO-friendly description (2 paragraphs). "
+        "Return clearly, titles separated by '||' then '---' then the description."
+    )
+    inputs = tokenizer(prompt, return_tensors="pt")
+    out = model.generate(**inputs, max_new_tokens=300)
+    text = tokenizer.decode(out[0], skip_special_tokens=True)
+    # Try to split results
+    if "||" in text:
+        parts = text.split("---")
+        titles = parts[0].split("||")
+        desc = parts[1].strip() if len(parts) > 1 else ""
+    else:
+        # fallback: guess
+        lines = [l.strip() for l in text.split("\n") if l.strip()]
+        titles = lines[:max_titles]
+        desc = "\n".join(lines[max_titles:])
+    titles = [t.strip() for t in titles if t.strip()]
+    return titles[:max_titles], desc
+
+# Gradio function
+def analyze(title, description, lang_choice):
+    text = (title or "") + "\n" + (description or "")
+    lang = "tr" if lang_choice == "Türkçe" else "en"
+    # extract keywords
+    keywords = extract_keywords(text, lang=lang, max_kw=8)
+    # generate suggestions
+    gen_titles, gen_desc = gen_suggestions(text if text.strip() else "Short video about ...", keywords)
+    # tags: use keywords as tags (shorten)
+    tags = [k.replace(" ", "_") for k in keywords][:12]
+    # score
+    score = seo_score(title, description, keywords, tags)
+    return {
+        "SEO Skoru (0-100)": score,
+        "Çıkarılan Anahtar Kelimeler": ", ".join(keywords),
+        "Önerilen Başlıklar": "\n".join([f"- {t}" for t in gen_titles]),
+        "Önerilen Açıklama": gen_desc,
+        "Önerilen Etiketler (tags)": ", ".join(tags)
+    }
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## YouTube SEO Asistanı — Basit & Ücretsiz (örnek)")
+    with gr.Row():
+        with gr.Column(scale=2):
+            title_in = gr.Textbox(label="Mevcut başlık (isteğe bağlı)", lines=1, placeholder="Var olan videonuzun başlığını yazın")
+            desc_in = gr.Textbox(label="Açıklama / Transcript / İçerik", lines=8, placeholder="Video açıklaması veya transkriptinizi buraya yapıştırın")
+            lang = gr.Radio(choices=["Türkçe", "English"], value="Türkçe", label="Dil")
+            btn = gr.Button("Analiz Et")
+        with gr.Column(scale=1):
+            score_out = gr.Label(num_top_classes=1, label="Sonuç")
+            result_box = gr.JSON(label="Detaylı Öneriler")
+    btn.click(fn=analyze, inputs=[title_in, desc_in, lang], outputs=[score_out, result_box])
+
+if __name__ == "__main__":
+    demo.launch()