UPD: Added main app

README.md

@@ -4,10 +4,10 @@ emoji: 🏢
 colorFrom: red
 colorTo: pink
 sdk: gradio
-sdk_version: 5.20.0
-app_file: app.py
+sdk_version: 5.22.0
+app_file: main.py
 pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

@@ -0,0 +1,179 @@
+import gradio
+import prompts
+import json
+from together import Together
+import base64
+import numpy as numpy
+from PIL import Image
+from io import BytesIO
+import uuid
+import datetime
+import os
+from huggingface_hub import HfApi
+
+
+HF_KEY = os.environ.get("HF_KEY") if os.environ.get("HF_KEY") else ""
+TOGETHER_KEY = os.environ.get("TOGETHER_KEY") if os.environ.get("TOGETHER_KEY") else ""
+PASSWORDS = os.environ.get("PASSWORDS") if os.environ.get("PASSWORDS") else ""
+
+hf_client = HfApi(
+    token = HF_KEY
+)
+
+together_client = Together(
+    api_key = TOGETHER_KEY
+)
+
+def process_token(secret_token):
+    global together_client
+    try:
+        passwords = PASSWORDS
+        passwords = passwords.split(":")
+        
+        if secret_token in passwords:
+            secret_token = TOGETHER_KEY
+        
+        together_client = Together(
+            api_key = secret_token
+        )
+        gradio.Info("API token has been set successfully.", duration = 2)
+        return secret_token
+    except:
+        return secret_token
+    
+    
+
+def assisted_prompt_generation(prompt):
+    gradio.Info("Assisting prompt generation...", duration = 2)
+    try:
+        response = together_client.chat.completions.create(
+            model = "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+            messages = [
+                {"role": "system", "content": prompts.assisted_prompt_generator.system_prompt},
+                {"role": "user", "content": f"{prompt}"},
+                {"role": "assistant", "content": ""}
+                ],
+            response_format = {"type": "json_object"}
+        )
+        output = json.loads(response.choices[0].message.content)
+        
+        if output["return_code"] == 400:
+            gradio.Error("Prompt generation failed.", duration = 5)
+            return output["prompt"]
+        else:
+            gradio.Info("Prompt generated successfully.", duration = 2)
+            return output["prompt"]
+    except Exception as e:
+        gradio.Error("Prompt generation failed.", duration = 5)
+        return "Failed"
+
+def verify_prompt(prompt):
+    gradio.Info("Veryfying prompt...", duration = 2)
+    try:
+        response = together_client.chat.completions.create(
+            model = "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+            messages = [
+                {"role": "system", "content": prompts.prompt_verification_agent.system_prompt},
+                {"role": "user", "content": f"{prompt}"},
+                {"role": "assistant", "content": ""}
+                ],
+            response_format = {"type": "json_object"}
+        )
+        output = json.loads(response.choices[0].message.content)
+        
+        if output["return_code"] == 400:
+            gradio.Error("Prompt verification failed.", duration = 5)
+            return "Failed"
+        else:
+            gradio.Info("Prompt verification successfully.", duration = 2)
+            return prompt
+    except Exception as e:
+        gradio.Error("Prompt verification failed.", duration = 5)
+        return "Failed"
+
+
+def generate_image(prompt):
+    if prompt == "Failed":
+        gradio.Error("Prompt generation failed.", duration = 5)
+        return numpy.zeros((1024, 1024, 3), dtype = numpy.uint8)
+    
+    response = together_client.images.generate(
+        prompt= prompt,
+        model = "black-forest-labs/FLUX.1-schnell-Free",
+        width = 1024,
+        height = 1024,
+        steps = 4,
+        n = 1,
+        response_format="b64_json",
+        stop=[]
+    )
+    b_64_image = response.data[0].b64_json
+    image_data = base64.b64decode(b_64_image)
+    image = Image.open(BytesIO(image_data))
+    image_np = numpy.array(image)
+    return image_np
+
+
+def save_image(prompt, image):
+    temp_id = uuid.uuid4()
+    datetime_now = datetime.datetime.now()
+    year = datetime_now.year
+    month = datetime_now.month
+    day = datetime_now.day
+    hour = datetime_now.hour
+    minute = datetime_now.minute
+    
+    image_PIL = Image.fromarray(image)
+    image_PIL.save(f"{temp_id}.png")
+    prompt = {
+        "prompt": prompt,
+    }
+    json.dump(prompt, open(f"{temp_id}.json", "w"))
+    hf_client.upload_file(
+        path_or_fileobj = f"{temp_id}.png",
+        path_in_repo = f"images/{year}/{month}/{day}/{hour}/{minute}/{temp_id}.png",
+        repo_type = "dataset",
+        repo_id = "xqt/fashion_model_generator",
+        commit_message = f"ADD: image {temp_id}.png",
+    )
+    hf_client.upload_file(
+        path_or_fileobj = f"{temp_id}.json",
+        path_in_repo = f"images/{year}/{month}/{day}/{hour}/{minute}/{temp_id}.json",
+        repo_type = "dataset",
+        repo_id = "xqt/fashion_model_generator",
+        commit_message = f"ADD: prompt {temp_id}.json",
+    )
+    gradio.Info(f"Image and prompt saved successfully at https://huggingface.co/datasets/xqt/fashion_model_generator/tree/main/images/{year}/{month}/{day}/{hour}/{minute}/{temp_id}.png", duration = 5)
+    os.remove(f"{temp_id}.png")
+    os.remove(f"{temp_id}.json")
+    return
+    
+
+with gradio.Blocks(fill_width = False) as app:
+    gradio.Markdown("""
+    # Fashion Model Generator
+    ## This app generates images of fashion model.
+    Synthetic Dataset: [xqt/fashion_model_generator](https://huggingface.co/datasets/xqt/fashion_model_generator)
+    """)
+    
+    api_token_input = gradio.Textbox(label = "Together AI API Key (key is never stored and it uses free models only)", placeholder = "Enter your Together AI API Key here.", type = "password")
+    
+    with gradio.Row(equal_height = True):
+        with gradio.Column(scale = 3):
+            prompt_input = gradio.Textbox(label = "Prompt", placeholder = "Enter your prompt here.")
+        with gradio.Column():
+            prompt_assist = gradio.Button(value = "Assisted Prompt Generation")
+    
+    image_output = gradio.Image(label="Generated Image")
+    
+    api_token_input.submit(process_token, inputs = [api_token_input], outputs = [api_token_input])
+    prompt_assist.click(assisted_prompt_generation, inputs = [prompt_input], outputs = [prompt_input])
+    prompt_input.submit(verify_prompt, inputs = [prompt_input], outputs = [prompt_input]).then(
+        generate_image, inputs = [prompt_input], outputs = [image_output]
+    ).then(
+        save_image, inputs = [prompt_input, image_output], outputs = []
+    )
+
+if __name__ == "__main__":
+    app.launch()
+    

prompts.py

@@ -0,0 +1,38 @@
+
+class assisted_prompt_generator:
+    system_prompt = """
+You are a prompt generation bot, your task is to generate a prompt for a given input. 
+The user will provide you with some inputs, you need to prepare a prompt based on the input.
+The prompt needs to be related to Fashion Models. Your prompt will be used to generate image of a fashion model.
+You need to imagine a background for the model in case the user does not provide one.
+If possible, try to formulate the prompt so that the model's hands are not visible in the image, unless the user specifies otherwise.
+If the prompt is inappropriate, return error 400. Return the response as a dictionary with the keys "return_code" and "prompt".
+
+For example:
+{
+    "return_code": 400,
+    "prompt": "Failed."
+}
+
+{
+    "return_code": 200,
+    "prompt": "The female is wearing a red dress and is standing in a garden."
+}
+"""
+
+class prompt_verification_agent:
+    system_prompt = """
+You are a prompt verification agent, your task is to verify the user prompt. The prompt must be relevant to the input
+for image generation of a fashion model. The prompt generation bot will provide you with a prompt, you need to verify if the prompt is appropriate or not.
+If the prompt is inappropriate, return error 400. Return the response as a dictionary with the keys "return_code" and "message".
+
+For example:
+{
+    "return_code": 400,
+    "message": "Failed"
+}
+{
+    "return_code": 200,
+    "message": "Success"
+}
+"""

requirements.txt

@@ -0,0 +1,4 @@
+gradio==5.20.0
+together==1.4.6
+huggingface-hub==0.29.3
+pillow==11.1.0