Create app.py

app.py

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+import streamlit as st
+from tensorflow.keras import layers, Model
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from rembg import remove
+import numpy as np
+import warnings
+warnings.filterwarnings("ignore")
+from PIL import Image
+from tensorflow.keras.utils import get_custom_objects
+import os
+from keras import backend as K
+from keras.saving import register_keras_serializable
+from deskew import determine_skew
+import cv2
+from googletrans import Translator
+import torch
+from diffusers import StableDiffusionImg2ImgPipeline
+from transformers import LlamaForCausalLM, PreTrainedTokenizerFast, pipeline
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer
+from transformers import pipeline
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TrainingArguments,
+    pipeline,
+    logging,
+)
+
+promt = None
+
+i_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to('cpu')
+torch.cuda.empty_cache()
+
+
+base_model = "meta-llama/Llama-3.2-1B" # https://huggingface.co/meta-llama/Llama-3.2-1B
+
+hf_dataset = "ahmeterdempmk/Llama-E-Commerce-Fine-Tune-Data" # https://huggingface.co/ahmeterdempmk/Llama-E-Commerce-Fine-Tune-Data
+
+dataset = load_dataset(hf_dataset, split="train")
+model = AutoModelForCausalLM.from_pretrained (
+    base_model,
+    device_map={"": 0}
+)
+model.config.use_cache = False
+model.config.pretraining_tp = 1
+model.low_cpu_mem_usage=True
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+peft_params = LoraConfig (
+    lora_alpha=16, # the scaling factor for the low-rank matrices
+    lora_dropout=0.1, # the dropout probability of the LoRA layers
+    r=64, # the dimension of the low-rank matrices
+    bias="none",
+    task_type="CAUSAL_LM", # the task to train for (sequence-to-sequence language modeling in this case)
+)
+training_params = TrainingArguments (
+    output_dir="./LlamaResults",
+    num_train_epochs=5, # One training epoch.
+    per_device_train_batch_size=4, # Batch size per GPU for training.
+    gradient_accumulation_steps=1, # This refers to the number of steps required to accumulate the gradients during the update process.
+    optim="paged_adamw_32bit", # Model optimizer (AdamW optimizer).
+    save_steps=25,
+    logging_steps=25,
+    learning_rate=2e-4, # Initial learning rate. (Llama 3.1 8B ile hesaplandı)
+    weight_decay=0.001, # Weight decay is applied to all layers except bias/LayerNorm weights.
+    fp16=False, # Disable fp16/bf16 training.
+    bf16=False, # Disable fp16/bf16 training.
+    max_grad_norm=0.3, # Gradient clipping.
+    max_steps=-1,
+    warmup_ratio=0.03,
+    group_by_length=True,
+    lr_scheduler_type="constant",
+    report_to="tensorboard"
+)
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    peft_config=peft_params,
+    dataset_text_field="input",
+    max_seq_length=None,
+    tokenizer=tokenizer,
+    args=training_params,
+    packing=False,
+)
+train_output = trainer.train()
+torch.cuda.empty_cache()
+
+languages = {
+    "Türkçe": "tr", 
+    "Azərbaycan dili": "az",  
+    "Deutsch": "de",          
+    "English": "en",          
+    "Français": "fr",        
+    "Español": "es",         
+    "Italiano": "it",        
+    "Nederlands": "nl",      
+    "Português": "pt",                
+    "Русский": "ru",         
+    "中文": "zh",             
+    "日本語": "ja",           
+    "한국어": "ko",           
+    "عربي": "ar",          
+    "हिन्दी": "hi",          
+    "ภาษาไทย": "th",       
+    "Tiếng Việt": "vi",      
+    "فارسی": "fa",         
+    "Svenska": "sv",         
+    "Norsk": "no",           
+    "Dansk": "da",
+    "Čeština": "cs",
+    "Ελληνικά": "el",   
+    "Bosanski": "bs",        
+    "Hrvatski": "hr",       
+    "Shqip": "sq", 
+    "Slovenčina": "sk",
+    "Slovenščina": "sl",
+    "Türkmençe": "tk", 
+    "български" : "bg",
+    "Кыргызча": "ky",          
+    "Қазақша": "kk",           
+    "Монгол": "mn",
+    "Українська": "uk",
+    "Cymraeg": "cy",
+    "Tatarça": "tt",
+    "Kiswahili": "sw",
+    "Hausa": "ha",
+    "አማርኛ": "am",
+    "Èdè Yorùbá": "yo",
+    "isiZulu": "zu",
+    "chiShona": "sn",
+    "isiXhosa": "xh"
+}
+
+
+tr_list = ["Lyra AI E-commerce Hackathon Project", "Select Model Sharpness", "Your Product", "Your Explanation About Your Product", "Generate", "Generated Image"]
+tr_list_tr = []
+@register_keras_serializable(package='Custom', name='mse')
+def custom_mse(y_true, y_pred):
+    return K.mean(K.square(y_true - y_pred))
+
+class STN(layers.Layer):
+    def __init__(self, **kwargs):
+        super(STN, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        self.localization = tf.keras.Sequential([
+            layers.Conv2D(16, (7, 7), activation='relu', input_shape=input_shape[1:]),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Conv2D(32, (5, 5), activation='relu'),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Flatten(),
+            layers.Dense(50, activation='relu'),
+            layers.Dense(6, activation='linear')
+        ])
+
+    def call(self, inputs):
+        theta = self.localization(inputs)
+        theta = tf.reshape(theta, [-1, 2, 3])
+        grid = self.get_grid(tf.shape(inputs), theta)
+        return self.sampler(inputs, grid)
+
+    def get_grid(self, input_shape, theta):
+        batch_size, height, width = input_shape[0], input_shape[1], input_shape[2]
+        x_coords = tf.linspace(-1.0, 1.0, width)
+        y_coords = tf.linspace(-1.0, 1.0, height)
+        x_grid, y_grid = tf.meshgrid(x_coords, y_coords)
+        ones = tf.ones_like(x_grid)
+        grid = tf.stack([x_grid, y_grid, ones], axis=-1)
+        grid = tf.reshape(grid, [1, height * width, 3])
+        grid = tf.tile(grid, [batch_size, 1, 1])
+        grid = tf.matmul(grid, tf.transpose(theta, [0, 2, 1]))
+        return grid
+
+    def sampler(self, inputs, grid):
+        shape = tf.shape(inputs)
+        batch_size = shape[0]
+        height = shape[1]
+        width = shape[2]
+        channels = shape[3]
+        resized_inputs = tf.image.resize(inputs, size=(height, width))
+        return resized_inputs
+
+get_custom_objects().update({'STN': STN})
+
+###!!!Functions Should Be Here!!!###
+
+def process_image(input_img):
+    input_img=input_img.resize((224,224)) 
+    input_img=np.array(input_img)
+    input_img=input_img/255.0
+    input_img=np.expand_dims(input_img,axis=0)
+    return input_img
+def blur_level(image):
+    if isinstance(image, Image.Image):
+        image = np.array(image)
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    laplacian = cv2.Laplacian(gray_image, cv2.CV_64F)
+    variance = laplacian.var()
+    return variance
+
+
+image_model = load_model("autoencoder.h5", custom_objects={'mse': custom_mse})
+torch.cuda.empty_cache()
+language = st.selectbox("Select Language", list(languages.keys()))
+
+if language:
+    translator = Translator()
+    tr_list_tr = [translator.translate(text, dest=languages[language]).text for text in tr_list]
+
+st.title(tr_list_tr[0])
+
+threshold = st.slider(tr_list_tr[1], min_value = 50, max_value = 100, value = 75)
+threshold=threshold*3
+img = st.camera_input(tr_list_tr[2])
+text = st.text_input(tr_list_tr[3])
+if st.button(tr_list[4]):
+    
+    if img and text is not None:
+        img=Image.open(img)
+        img1=remove(img)
+        if img1.mode == 'RGBA':
+            img1 = img1.convert('RGB')
+        input_img = process_image(img1)
+        torch.cuda.empty_cache()
+        prediction = image_model.predict(input_img)
+        pred_img = np.clip(prediction[0], 0, 1) * 255 
+        pred_img = Image.fromarray(pred_img.astype('uint8')) 
+        level =  blur_level(pred_img)
+        #st.write(level, threshold)
+        prompt = f"""
+You are extracting product title and description from given text and rewriting the description and enhancing it when necessary.
+Always give response in the user's input language.
+Always answer in the given json format. Do not use any other keywords. Do not make up anything.
+Explanations should contain at least three sentences each.
+
+Json Format:
+{{
+"title": "<title of the product>",
+"description": "<description of the product>"
+}}
+
+Examples:
+
+Product Information: Rosehip Marmalade, keep it cold
+Answer: {{"title": "Rosehip Marmalade", "description": "You should store this delicisious roseship marmelade in cold conditions. You can use it in your breakfasts and meals."}}
+
+Product Information: Blackberry jam spoils in the heat
+Answer: {{"title": "Blackberry Jam", "description": "Please store it in cold conditions. Recommended to be consumed at breakfast. Very sweet."}}
+
+Now answer this:
+Product Information: {text}"""
+        pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=10000)
+        torch.cuda.empty_cache()
+        if level < threshold:
+            if img.mode == 'RGB':
+                img = img.convert('RGB')
+            init_image = img.thumbnail((768, 768))
+            i_prompt = "Remove the background from the image and correct the perspective of the subject to ensure a straight and clear view."
+            images = i_pipe(prompt=i_prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+            images[0].save("output.png")
+            image = Image.open("./output.png")
+            st.image(image, caption=tr_list_tr[5], use_column_width=True)
+            result = pipe(f"Prompt: {prompt} \n Response:") # result = pipe(f"Prompt: {prompt} \n Response:")
+            generated_text = result[0]['generated_text']
+            st.write(generated_text)
+
+        else:
+            st.image(pred_img, caption=tr_list_tr[2], use_column_width=True)
+            result = pipe(f"Prompt: {prompt} \n Response:") # result = pipe(f"Prompt: {prompt} \n Response:")
+            generated_text = result[0]['generated_text']
+            st.write(generated_text)