Delete core.py

core.py

@@ -1,81 +0,0 @@
-# core.py (All logic here)
-import os
-import time
-import tempfile
-import torch
-from transformers import (
-    MBartForConditionalGeneration,
-    MBart50TokenizerFast,
-    AutoTokenizer,
-    AutoModelForCausalLM
-)
-from diffusers import StableDiffusionPipeline
-from PIL import Image
-from rouge_score import rouge_scorer
-
-# Device setup
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# HF token from env
-token = os.getenv("HF_TOKEN")
-if not token:
-    raise ValueError("❌ Please set your HF_TOKEN in the HF Spaces secrets.")
-
-# Load models
-translator_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
-translator_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt").to(device)
-translator_tokenizer.src_lang = "ta_IN"
-
-gen_tokenizer = AutoTokenizer.from_pretrained("gpt2")
-gen_model = AutoModelForCausalLM.from_pretrained("gpt2").to(device)
-
-pipe = StableDiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1",
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    use_auth_token=token
-).to(device)
-pipe.safety_checker = None
-
-
-def translate_tamil_to_english(text, reference=None):
-    start = time.time()
-    inputs = translator_tokenizer(text, return_tensors="pt").to(device)
-    outputs = translator_model.generate(
-        **inputs,
-        forced_bos_token_id=translator_tokenizer.lang_code_to_id["en_XX"]
-    )
-    translation = translator_tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
-    duration = round(time.time() - start, 2)
-
-    rouge_l = None
-    if reference:
-        scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
-        scores = scorer.score(reference.lower(), translation.lower())
-        rouge_l = round(scores['rougeL'].fmeasure, 4)
-
-    return translation, duration, rouge_l
-
-
-def generate_creative_text(prompt, max_length=100):
-    start = time.time()
-    input_ids = gen_tokenizer.encode(prompt, return_tensors="pt").to(device)
-    out = gen_model.generate(
-        input_ids, max_length=max_length, do_sample=True, top_k=50, temperature=0.9
-    )
-    text = gen_tokenizer.decode(out[0], skip_special_tokens=True)
-    duration = round(time.time() - start, 2)
-    tokens = text.split()
-    repetition = sum(t1 == t2 for t1, t2 in zip(tokens, tokens[1:])) / len(tokens)
-    return text, duration, len(tokens), round(repetition, 4)
-
-
-def generate_image(prompt):
-    try:
-        start = time.time()
-        out = pipe(prompt)
-        img = out.images[0].resize((256, 256))
-        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-        img.save(tmp.name)
-        return tmp.name, round(time.time() - start, 2)
-    except Exception as e:
-        return None, f"Image generation failed: {e}"