abdala

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled31 (2).ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1jx1zW74zl2vFolee01ukC1b11uyTJDZ4
+"""
+
+pip install -r requirements.txt
+
+pip install gradio
+
+import os
+
+from datasets import load_dataset
+
+# download dataset
+dataset = load_dataset("neuralwork/fashion-style-instruct")
+print(dataset)
+
+# print a sample triplet
+print(dataset["train"][0])
+
+def format_instruction(sample):
+    return f"""You are a personal stylist recommending fashion advice and clothing combinations. Use the self body and style description below, combined with the event described in the context to generate 5 self-contained and complete outfit combinations.
+        ### Input:
+        {sample["input"]}
+
+        ### Context:
+        {sample["context"]}
+
+        ### Response:
+        {sample["completion"]}
+    """
+
+sample = dataset["train"][0]
+print(format_instruction(sample))
+
+import os
+import random
+
+import torch
+import gradio as gr
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+
+
+events = [
+    "nature retreat",
+    "work / office event",
+    "wedding as a guest",
+    "tropical vacation",
+    "conference",
+    "sports event",
+    "winter vacation",
+    "beach",
+    "play / concert",
+    "picnic",
+    "night club",
+    "national parks",
+    "music festival",
+    "job interview",
+    "city tour",
+    "halloween party",
+    "graduation",
+    "gala / exhibition opening",
+    "fancy date",
+    "cruise",
+    "casual gathering",
+    "concert",
+    "cocktail party",
+    "casual date",
+    "business meeting",
+    "camping / hiking",
+    "birthday party",
+    "bar",
+    "business lunch",
+    "bachelorette / bachelor party",
+    "semi-casual event",
+]
+
+
+def format_instruction(input, context):
+    return f"""You are a personal stylist recommending fashion advice and clothing combinations. Use the self body and style description below, combined with the event described in the context to generate 5 self-contained and complete outfit combinations.
+        ### Input:
+        {input}
+
+        ### Context:
+        I'm going to a {context}.
+
+        ### Response:
+    """
+
+
+def main():
+    # load base LLM model, LoRA params and tokenizer
+    model = AutoPeftModelForCausalLM.from_pretrained(
+        "neuralwork/mistral-7b-style-instruct",
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.float16,
+        load_in_4bit=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained("neuralwork/mistral-7b-style-instruct")
+
+    def postprocess(outputs, prompt):
+        outputs = outputs.detach().cpu().numpy()
+        output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+        output = output[len(prompt) :]
+        return output
+
+    def generate(
+        prompt: str,
+        event: str,
+
+    ):
+        torch.manual_seed(1347)
+        prompt = format_instruction(str(prompt), str(event))
+        input_ids = tokenizer(
+            prompt, return_tensors="pt", truncation=True
+        ).input_ids.cuda()
+
+        with torch.inference_mode():
+            outputs = model.generate(
+                input_ids=input_ids,
+                max_new_tokens=1500,
+                min_new_tokens=10,
+                do_sample=True,
+                top_p=0.9,
+                temperature=.9,
+            )
+
+        output = postprocess(outputs, prompt)
+        return output
+
+    with gr.Blocks() as demo:
+        gr.HTML(
+            """
+            <h1 style="font-weight: 900; margin-bottom: 7px;">
+            Instruct Fine-tune Mistral-7B-v0
+            </h1>
+            <p>Mistral-7B-v0 fine-tuned on the <a href="https://huggingface.co/datasets/neuralwork/fashion-style-instruct">neuralwork/style-instruct</a> dataset.
+            To use the model, simply describe your body type and personal style and select the type of event you're planning to go.
+            <br/>
+            See our <a href="https://neuralwork.ai/">blog post</a> for a detailed tutorial to fine-tune Mistral on your own dataset.
+            <p/>"""
+        )
+        with gr.Row():
+            with gr.Column(scale=1):
+                prompt = gr.Textbox(
+                    lines=4,
+                    label="Style prompt, describe your body type and fashion style.",
+                    interactive=True,
+                    value="I'm an above average height athletic woman with slightly of broad shoulders and a medium sized bust. I generally prefer a casual but sleek look with dark colors and jeans.",
+                )
+                event = gr.Dropdown(
+                    choices=events, value="semi-casual event", label="Event type"
+                )
+
+                generate_button = gr.Button("Get outfit suggestions")
+
+            with gr.Column(scale=2):
+                response = gr.Textbox(
+                    lines=6, label="Outfit suggestions", interactive=False
+                )
+
+        gr.Markdown("From [neuralwork](https://neuralwork.ai/) with :heart:")
+
+        generate_button.click(
+            fn=generate,
+            inputs=[
+                prompt,
+                event,
+
+            ],
+            outputs=response,
+        )
+
+    demo.launch(share=True)
+
+
+if __name__ == "__main__":
+    main()
+