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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+# Install dependencies
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the app
+COPY . /code
+
+# Set permissions (Required by HF Spaces)
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Run the API on port 7860
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import nltk
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from transformers import AutoTokenizer, pipeline
+from rake_nltk import Rake
+
+# --- Setup & Model Loading ---
+app = FastAPI()
+
+# CRITICAL: Allow your Frontend to access this API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins (Safe for public free APIs)
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+print("Loading Models...")
+# Download NLTK data
+try:
+    nltk.data.find('tokenizers/punkt_tab')
+except LookupError:
+    nltk.download('stopwords')
+    nltk.download('punkt_tab')
+
+# Load AI Models (Cached in memory)
+t5_tokenizer = AutoTokenizer.from_pretrained("Michau/t5-base-en-generate-headline", use_fast=False)
+title_pipe = pipeline("text2text-generation", model="Michau/t5-base-en-generate-headline", tokenizer=t5_tokenizer)
+
+bart_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn", use_fast=False)
+desc_pipe = pipeline("summarization", model="facebook/bart-large-cnn", tokenizer=bart_tokenizer)
+print("Models Ready!")
+
+# --- Logic ---
+class VideoInput(BaseModel):
+    text: str
+
+def get_tags(text):
+    rake = Rake()
+    rake.extract_keywords_from_text(text)
+    phrases = rake.get_ranked_phrases()[:5]
+    hashtags = ["#" + p.replace(" ", "") for p in phrases]
+    tags = [p.replace(" ", "") for p in phrases]
+    return hashtags, tags
+
+@app.get("/")
+def home():
+    return {"status": "API is running. POST to /generate"}
+
+@app.post("/generate")
+async def generate(payload: VideoInput):
+    text = payload.text
+    if not text.strip():
+        raise HTTPException(status_code=400, detail="Empty text")
+
+    # 1. Generate Title
+    title_out = title_pipe("headline: " + text, max_new_tokens=70, do_sample=False)
+    
+    # 2. Generate Description
+    desc_out = desc_pipe(text, max_new_tokens=150, do_sample=False)
+    
+    # 3. Get Tags
+    hashtags, tags = get_tags(text)
+
+    return {
+        "title": title_out[0]["generated_text"],
+        "description": desc_out[0]["summary_text"],
+        "hashtags": hashtags,
+        "tags": tags
+    }

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+sentencepiece
+rake-nltk
+nltk