app.py

import os
from huggingface_hub import login
from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import MllamaForConditionalGeneration, AutoProcessor
from PIL import Image
from dotenv import load_dotenv

import gradio as gr
from diffusers import DiffusionPipeline
import torch
import spaces  # Hugging Face Spaces module

import requests
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

from diffusers import DiffusionPipeline



fabrics = ['cotton', 'silk', 'denim', 'linen', 'polyester', 'wool', 'velvet']
patterns = ['striped', 'floral', 'geometric', 'abstract', 'solid', 'polka dots']
textile_designs = ['woven texture', 'embroidery', 'printed fabric', 'hand-dyed', 'quilting']




# Get Hugging Face Token from environment variable
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")

# Authenticate using the token
login(token ="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")
if not hf_token:
    raise ValueError("Hugging Face token is not set in the environment variables.")
login(token=hf_token)




model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"

model = MllamaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

# Load the processor and model
# processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
# model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
# processor1 = BlipProcessor.from_pretrained("noamrot/FuseCap")
# model2 = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap")
# pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium")
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)




device = "cuda" if torch.cuda.is_available() else "cpu"
# pipe.to(device)

model.to(device)
pipe.to(device)



@spaces.GPU(duration=150)
def generate_caption_and_image(image, f, p, d):
    if f!=None and p!=None and d!=None and image!=None:
        img = image.convert("RGB")
        # reader = easyocr.Reader(['en'])  
        # # result = reader.readtext(img)
        # import random
        
    
    
    
    
            
    
     
        # text = "a picture of "
        # inputs = processor(img, text, return_tensors="pt").to(device)
        
        # out = model2.generate(**inputs, num_beams = 3)
       
        
    
        # caption2 = processor.decode(out[0], skip_special_tokens=True)
        
        # Generate caption
        # inputs = processor(image, return_tensors="pt", padding=True, truncation=True, max_length=250)
        # inputs = {key: val.to(device) for key, val in inputs.items()}
        # out = model.generate(**inputs)
        # caption1 = processor.decode(out[0], skip_special_tokens=True)
    
        # prompt = f"Design a high-quality, stylish clothing item that seamlessly blends the essence of {caption1} and {caption2}. The design should prominently feature {f}{d} and incorporate {p}. The final piece should exude sophistication and creativity, suitable for modern trends while retaining an element of timeless appeal. Ensure the textures and patterns complement each other harmoniously, creating a visually striking yet wearable garment."




        
    
    
      
    
        # # Generate image based on the caption
        # generated_image = pipe(prompt).images[0]
        # generated_image1 =pipe(prompt).images[0]
    
        # return generated_image, generated_image1
        messages = [{"role": "user", "content": [{"type": "image"},{"type": "text", "text": "If I had to write a haiku for this one, it would be: "}]}]
        input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
        inputs = processor(img,input_text,add_special_tokens=False,return_tensors="pt").to(device)
        
        output = model.generate(**inputs, max_new_tokens=30)
        caption =processor.decode(output[0])
        image = pipe(caption,height=1024,width=1024,guidance_scale=3.5,num_inference_steps=50,max_sequence_length=512,generator=torch.Generator("cpu").manual_seed(0)).images[0]
        return image
    return None
# Gradio UI
iface = gr.Interface(
    fn=generate_caption_and_image,
    inputs=[gr.Image(type="pil", label="Upload Image"), gr.Radio(fabrics, label="Select Fabric"), gr.Radio(patterns, label="Select Pattern"), gr.Radio(textile_designs, label="Select Textile Design")],
            
    outputs=[gr.Image(label="Generated Design 1")],
    live=True
)
iface.launch(share=True)