Create app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import yake
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# --- Model (small, CPU-friendly)
+MODEL_NAME = "google/flan-t5-small"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+# Keyword extractor
+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)
+    return [kw for kw, score in kws]
+
+# Simple SEO scoring
+def seo_score(title, description, keywords, tags):
+    score = 0
+    # Title rules
+    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
+    return min(100, score)
+
+# Generate titles & description
+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"Content: {main_text}\n"
+        f"Keywords: {', '.join(keywords)}\n"
+        "Produce 3 short titles (<70 chars each) separated by || and one SEO-friendly description after ---"
+    )
+    inputs = tokenizer(prompt, return_tensors="pt")
+    out = model.generate(**inputs, max_new_tokens=300)
+    text = tokenizer.decode(out[0], skip_special_tokens=True)
+    # Split
+    if "||" in text:
+        parts = text.split("---")
+        titles = parts[0].split("||")
+        desc = parts[1].strip() if len(parts) > 1 else ""
+    else:
+        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
+
+# Main function
+def analyze(title, description, lang_choice):_