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app.py

@@ -1,588 +1,10 @@
-# Dependencies: gradio, fire, langchain, openai, numpy, ffmpeg, moviepy
-# API Reference: https://www.gradio.app/docs/,
-# https://github.com/zhayujie/chatgpt-on-wechat, https://docs.link-ai.tech/platform/api,  https://docs.link-ai.tech/api#/
-# Description: This file contains the code to run the gradio app for the movie generator.
-# 
-#
-#
-# 参考链接： https://zhuanlan.zhihu.com/p/684798694
-#
-#
-####################################################################################################
-
-import gradio as gr
-import fire
-from gradio_client import Client, file
-import numpy as np
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import AIMessage, HumanMessage
-
-from openai import OpenAI
-import os
-import moviepy.editor as mppyth
-from moviepy.editor import *
-# from movie_generator.agi.suno.suno import Suno
-import requests
-
-
-import ollama
-from ollama import chat
-from ollama import ChatResponse
-
-# ollama.pull("deepseek-r1:1.5b")
-# print( 'ollama result:',ollama.list())
-# response: ChatResponse = chat(model='deepseek-r1:1.5b', messages=[
-#   {
-#     'role': 'user',
-#     'content': 'Why is the sky blue?',
-#   },
-# ])
-# print(response['message']['content'])
-# # or access fields directly from the response object
-# print(response.message.content)
-
-def call_LLM(inputs, prompts= '你是一个时尚服装行业的专家， 请回答下面问题：', model_version = 'Qwen'):
-    inputs = prompts + ' ' + inputs
-    if model_version=="Qwen":
-        from openai import OpenAI
-
-        model_id = 'Qwen/Qwen2.5-3B-Instruct-GGUF'
-
-        client = OpenAI(
-            base_url='https://ms-fc-2ea3820b-8c19.api-inference.modelscope.cn/v1',
-            api_key='e37bfdad-0f6a-46c2-a7bf-f9dc365967e3'
-        )
-
-        response=client.chat.completions.create(
-            model=model_id,
-            messages=[{"role":"user", "content":inputs}],
-            stream=True
-        )
-
-        res= []
-        for chunk in response:
-            # print(chunk.choices[0].delta.content, end='', flush=True)
-            res.append(chunk.choices[0].delta.content)
-        return "".join(res)
-    elif model_version in ['deepseek-r1:1.5b', 'llama3.2:latest']: 
-        
-        # model= 'deepseek-r1:1.5b'
-        # model = 'llama3.2:latest'
-        response: ChatResponse = chat(model= model_version, messages=[
-        {
-            'role': 'user',
-            'content': prompts + " " + inputs,
-        },
-        ])
-        return response['message']['content']
-    else:
-        return "LLM version is not supported yet."
-import os
-class GradioApp:
-    def __init__(self,config=None):
-        #config with info of 
-        # model version
-        # prompts
-        #others
-        self.config=config
-        # self.image_dir = "/mnt/d/workspace/projects/Project_TextImage_Generator/examples"
-        self.image_dir = "../examples"
-        self.model_dir = os.path.join(self.image_dir, "models")
-        self.clothes_dir = os.path.join(self.image_dir, "clothes")
-        self.reference_dir = os.path.join(self.image_dir, "references")
-        self.model_files = [os.path.join(self.model_dir, f) for f in os.listdir(self.model_dir)]
-        self.clothes_files = [os.path.join(self.clothes_dir, f) for f in os.listdir(self.clothes_dir)]
-        self.reference_files = [os.path.join(self.reference_dir, f) for f in os.listdir(self.reference_dir)]
-        pass
-    
-    
-    def test_image_func(self, input_image, filter_mode='sepia'):
-        def filter_image(input_image, filter_mode='sepia'):
-            def sepia(input_img):
-                sepia_filter = np.array([
-                    [0.393, 0.769, 0.189], 
-                    [0.349, 0.686, 0.168], 
-                    [0.272, 0.534, 0.131]
-                ])
-                sepia_img = input_img.dot(sepia_filter.T)
-                sepia_img /= sepia_img.max()
-                return sepia_img
-            def grayscale(input_img):
-                input_img = np.mean(input_img, axis=2) / np.max(input_img)
-                return input_img
-            if filter_mode == 'sepia':
-                return sepia(input_image)
-            elif filter_mode == 'grayscale':
-                return grayscale(input_image)
-            else:
-                return input_image
-        res = f"Got image from image input: {input_image}"
-        filtered_image = filter_image(input_image, filter_mode)
-        return res, filtered_image
-    
-    def dress_up_func(self, model_images, cloths_images, prompts, similarity):
-        # 请求GPT response
-        return "dress_up_func output",[(model_images, "模特"), (cloths_images, "衣服")]*5
-
-    def update_model_func(self, model_images, cloths_images, prompts, similarity):
-        # 请求GPT response
-        return "update_model_func output", [(model_images, "模特"), (cloths_images, "衣服")]*5
-    
-    def image_module(self, mode='dress_up', title='image_module', desc=''):
-        if mode == 'dress_up':
-            # 模特试衣
-            func = self.dress_up_func
-        elif mode == 'update_model':
-            # 更新模特
-            func = self.update_model_func
-        else:
-            func = self.dress_up_func
-        examples = []
-        for i, (c, m) in enumerate( zip(self.clothes_files, self.model_files) ):
-            examples.append([c, m, 'sepia', 0.6] )
-        comp = gr.Interface(
-                fn= func,
-                inputs=[gr.Image(label='衣服', scale=1, height=300),
-                        gr.Image(label='模特',scale=1, height=300),
-                        gr.Dropdown(['sepia', 'grayscale']),
-                        gr.Slider(0, 10, value=5, label="相似度控制", info="similarity between 2 and 20")],
-                outputs=[gr.Textbox(label="文本输出"),
-                         gr.Gallery(label='图片展示',height='auto',columns=3)
-                         ],
-                title=title,
-                description=desc,
-                theme="huggingface",
-                examples=examples,
-            )
-        return comp
-    
-    def image_module_v2(self, mode='dress_up', title='image_module', desc=''):
-        def upload_file(files, current_files):
-            file_paths = current_files + [file.name for file in files]
-            return file_paths
-
-        def gen_images(clothes_img, model_img):
-            new_images = []
-            #call LLM/SD here
-            new_images.append(clothes_img)
-            new_images.append(model_img)
-            return new_images
-        
-        def clear_images():
-            return []
-        def slider_func(val):
-            print("slider value: ", val)
-
-
-        if mode == 'dress_up':
-            # 模特试衣
-            func = self.dress_up_func
-        elif mode == 'update_model':
-            # 更新模特
-            func = self.update_model_func
-        else:
-            func = self.dress_up_func
-
-        with gr.Blocks() as demo:
-            # first row
-            with gr.Row():
-                # first col -> input column
-                with gr.Column():
-                    model_image=gr.Image(label="模特图片",type='pil', height=None, width=None)
-                    clothes_image=gr.Image(label="衣服图片",type='pil', height=None, width=None)
-                    upload_button = gr.UploadButton("选择图片上传 (Upload Photos)", file_types=["image"], file_count="multiple")
-                    generate_img_button = gr.Button("生成图片")
-                    slider = gr.Slider(0, 10, value=5, label="相似度控制", info="similarity between 2 and 20")
-                    clear_button = gr.Button("清空图片 (Clear Photos)")
-                    
-                    # analyze_button = gr.Button("显示图片信息 (Show Image Info)")
-                    input_image_gallery = gr.Gallery(type='pil', label='输入图片列表 (Photos)', height=250, columns=4, visible=True)
-                # second col-> output column
-                with gr.Column():
-                    image_gallery = gr.Gallery(type='pil', label='图片列表 (Photos)', height=250, columns=4, visible=True)
-            # user_images = gr.State([])
-            # upload_button.upload(upload_file, inputs=[upload_button, user_images], outputs=image_gallery)
-            slider.input(fn=slider_func)
-            generate_img_button.click(gen_images,inputs=[clothes_image, model_image], outputs= image_gallery)
-            clear_button.click(fn=clear_images, inputs=None, outputs=image_gallery)
-            # analyze_button.click(get_image_info, inputs=image_gallery, outputs=analysis_output)
-            return demo
-
-    def gen_text(self,inputs, LLM_version='Qwen'):
-        # 设置前置prompt做限制
-        prompts = "你是一个时尚服装行业的专家， 请回答下面问题,只罗列答案不要返回多余的词："
-        # model= 'deepseek-r1:1.5b'
-        # return call_LLM(inputs,prompts, model_version='llama3.2:latest')
-        return call_LLM(inputs,prompts, model_version=LLM_version)
-    
-    def text_module(self, title='文本生成', desc="AI生成关键词"):
-        comp = gr.Interface(
-                fn= self.gen_text,
-                inputs=[gr.Textbox(label="文本输入"), gr.Dropdown(['deepseek-r1:1.5b', 'llama3.2:latest','Qwen'], label='模型选择')],
-                outputs=[gr.Textbox(label="结果输出")],
-                title=title,
-                description=desc,
-                theme="huggingface",
-                examples=[
-                    ["列出2024年最受欢迎的10个衣服品牌","llama3.2:latest"],
-                      ["哪些款式的女装比较潮流， 请列出10个女装品类","Qwen"],
-                      ["随机生成10个衣服类目并列出来","Qwen"]],
-                cache_examples=True,
-            )
-        return comp
-    
-    def generate_interface(self,):
-        tab_interface_ls = {}
-        # module 1: 生词
-        tab_interface_ls['AI生词'] = self.text_module()
-
-        # module 2: 服装上身
-        tab_interface_ls['服装搭配'] = self.image_module('dress_up', title="服装搭配")
-           
-        # module 3: 换模特
-        tab_interface_ls['更换模特'] = self.image_module('update_model', title="更换模特")
-
-        comp = gr.TabbedInterface(
-                list(tab_interface_ls.values()), list(tab_interface_ls.keys())
-            )
-        return comp
-
-def main():
-    print(f"Runing Gradio APP")
-    component = GradioApp()
-    component.generate_interface().launch(share=True)
-
-
-if __name__ == "__main__":
-    main()
-
-
-
-
-# class GradioUnitTest():
-#     def __init__(self):
-#         api_key =  "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"
-#         # "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"
-#         os.environ["OPENAI_API_KEY"] = api_key
-#         self.llm_model = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
-#         # self.llm_model= None
-#         self.client = OpenAI(api_key=api_key)
-#         cur_path =os.getcwd()
-#         root_path = '/'.join(cur_path.split("/")[:-2])
-
-#         suno_result_path = os.path.join(root_path,'examples','suno_musics')
-#         # self.suno = Suno(result_path=suno_result_path)
-#         self.suno= None
-#         pass
-    
-#     def test_text(self, input_text, mode = 'count'):
-#         def process_test( _text, mode = 'count'):
-#             def count_words(text):
-#                 words = text.split(" ")
-#                 res_dict = {}
-#                 for word in words:
-#                     if word in res_dict:
-#                         res_dict[word] += 1
-#                     else:
-#                         res_dict[word] = 1
-#                 res = "\n".join([f"word: {key}, count: {value}" for key, value in res_dict.items()])
-#                 return res
-            
-#             def reverse_text(text):
-#                 return text[::-1]
-            
-#             if mode == 'count':
-#                 return count_words(_text)
-#             return reverse_text(_text)
-
-#         res = f"Got text from textbox: {input_text}"
-#         return res, process_test(input_text, mode)
-#         # return res, count_words(input_text)
-    
-#     def test_image(self, input_image, filter_mode='sepia'):
-#         def filter_image(input_image, filter_mode='sepia'):
-#             def sepia(input_img):
-#                 sepia_filter = np.array([
-#                     [0.393, 0.769, 0.189], 
-#                     [0.349, 0.686, 0.168], 
-#                     [0.272, 0.534, 0.131]
-#                 ])
-#                 sepia_img = input_img.dot(sepia_filter.T)
-#                 sepia_img /= sepia_img.max()
-#                 return sepia_img
-#             def grayscale(input_img):
-#                 input_img = np.mean(input_img, axis=2) / np.max(input_img)
-#                 return input_img
-            
-#             if filter_mode == 'sepia':
-#                 return sepia(input_image)
-#             elif filter_mode == 'grayscale':
-#                 return grayscale(input_image)
-#             else:
-#                 return input_image
-#         res = f"Got image from image input: {input_image}"
-#         filtered_image = filter_image(input_image, filter_mode)
-#         return res, filtered_image
-    
-#     def test_audio(self, input_audio, filter_mode='echo', prompt='', checkbox_ls=[]):
-#         def process_audio(input_audio, filter_mode='echo'):
-#             print("input_audio shape: ", input_audio[1].shape, input_audio)
-#             def echo(input_audio):
-#                 aud = np.concatenate([input_audio[1], input_audio[1]], axis=0)
-#                 return (input_audio[0], aud)
-#             def reverse(input_audio):
-#                 return (input_audio[0], input_audio[1][::-1]) 
-            
-#             if filter_mode == 'echo':
-#                 res_audio = echo(input_audio)
-#             elif filter_mode == 'reverse':
-#                 res_audio = reverse(input_audio)
-#             else:
-#                 res_audio = input_audio
-#             return res_audio
-#         print("checkbox_ls: ", checkbox_ls)
-#         res = f"Got audio from audio input: {input_audio}"
-#         wait_audio = 'wait_audio' in checkbox_ls
-#         make_instrumental = 'make_instrumental' in checkbox_ls
-#         if checkbox_ls != []:
-#             print('checlbox_ls: ', checkbox_ls)
-#         generated_audio_path=''
-#         if prompt != '':
-#             music_paths = self.test_music_generation(prompt, make_instrumental, wait_audio)
-#             generated_audio_path = '\n'.join(music_paths)
-#             res = f"Got audio from suno: {generated_audio_path}"
-#         processed_audio = process_audio(input_audio, filter_mode)
-#         return res, processed_audio, generated_audio_path
-
-#     def test_video(self, input_video, filter_mode='flip'):
-#         def process_video(input_video, filter_mode='flip'):
-#             print("input_video data: ", input_video)
-
-#             def clip(input_video):
-#                 clip1 = VideoFileClip(input_video)
-#                 clip2 = VideoFileClip(input_video).subclip(2,3)
-#                 clip3 = VideoFileClip(input_video)
-#                 final_clip = concatenate_videoclips([clip1,clip2,clip3])
-#                 output_video = "final_clip.mp4"
-#                 final_clip.write_videofile(output_video)
-#                 return output_video
-#             def flip(input_video):
-#                 return np.flip(input_video, axis=1)
-#             def rotate(input_video):
-#                 return np.rot90(input_video)
-#             if filter_mode == 'clip':
-#                 return clip(input_video)
-#             elif filter_mode == 'flip':
-#                 return flip(input_video)
-#             elif filter_mode == 'rotate':
-#                 return rotate(input_video)
-#             else:
-#                 return input_video
-#         res = f"Got video from video input: {input_video}"
-#         processed_video = process_video(input_video, filter_mode)
-#         return res, processed_video
-
-#     def test_chatbot(self, input_text, history):
-#         history_langchain_format =[]
-#         for human, ai in history:
-#             history_langchain_format.append(HumanMessage(human))
-#             history_langchain_format.append(AIMessage(ai))
-#         history_langchain_format.append(content=input_text)
-#         llm_response = self.llm_model(history_langchain_format)
-#         return llm_response.content
-
-#     def predict(self, message, history):
-#         history_openai_format = []
-#         for human, assistant in history:
-#             history_openai_format.append({"role": "user", "content": human })
-#             history_openai_format.append({"role": "assistant", "content":assistant})
-#         history_openai_format.append({"role": "user", "content": message})
-    
-#         response = self.client.chat.completions.create(model='gpt-3.5-turbo',
-#         messages= history_openai_format,
-#         temperature=1.0,
-#         stream=True)
-
-#         partial_message = ""
-#         for chunk in response:
-#             if chunk.choices[0].delta.content is not None:
-#                 partial_message = partial_message + chunk.choices[0].delta.content
-#                 yield partial_message
-    
-#     def predict_v2(self, message, history):
-        
-#         url = "https://api.link-ai.chat/v1/chat/completions"
-#         headers = {
-#             'Authorization': 'Bearer Link_USN4Vru40ciqYkdpeWywmOOIOPHGLYm8EuAGm0xE0b',
-#             'Content-Type': 'application/json'
-#         }
-#         history_openai_format = []
-#         for human, assistant in history:
-#             history_openai_format.append({"role": "user", "content": human })
-#             history_openai_format.append({"role": "assistant", "content":assistant})
-#         history_openai_format.append({"role": "user", "content": message})
-    
-
-#         data = {
-#             "app_code": "default",
-#             "messages": history_openai_format,
-#         }
-
-#         response = requests.post(url, headers=headers, json=data).json()
-#         partial_message = ""
-#         for chunk in response['choices']:
-#             if chunk['message']["content"] is not None:
-#                 partial_message = partial_message + chunk['message']["content"]
-#                 yield partial_message
-    
-
-#     def predict_v3(self, message, history):
-        
-#         url = "https://api.link-ai.chat/v1/chat/completions"
-#         headers = {
-#             'Authorization': 'Bearer Link_USN4Vru40ciqYkdpeWywmOOIOPHGLYm8EuAGm0xE0b',
-#             'Content-Type': 'application/json'
-#         }
-#         history_openai_format = []
-#         for human, assistant in history:
-#             history_openai_format.append({"role": "user", "content": human })
-#             history_openai_format.append({"role": "assistant", "content":assistant})
-#         history_openai_format.append({"role": "user", "content": message})
-    
-
-#         data = {
-#             "app_code": "default",
-#             "messages": history_openai_format,
-#         }
-
-#         response = requests.post(url, headers=headers, json=data).json()
-#         partial_message = ""
-#         for chunk in response['choices']:
-#             if chunk['message']["content"] is not None:
-#                 partial_message = partial_message + chunk['message']["content"]
-#                 yield partial_message
-
-#     def test_music_generation(self, prompt, make_instrumental=False, wait_audio=False):
-#         request = {
-#             "prompt": prompt,
-#             "make_instrumental": make_instrumental,
-#             "wait_audio": wait_audio
-#             }
-#         # music_ls = self.suno.generate_music(request)
-#         music_ls = []
-#         return music_ls
-
-#     def run_test(self, mode='text'):
-#         tab_interface_ls = {}
-#         if mode == 'text' or mode == 'mix':
-#             comp = gr.Interface(
-#                 fn= self.test_text,
-#                 inputs=['textbox', gr.Dropdown(['count', 'reverse'])],
-#                 outputs=["textbox", "textbox"],
-#                 title="test text module",
-#                 description="test text.",
-#                 theme="huggingface",
-#                 examples=[
-#                     ["A group of friends go on a road trip to find a hidden treasure."],
-#                     ["A scientist discovers a way to travel through time."],
-#                     ["A group of survivors try to escape a zombie apocalypse."],
-#                 ],
-#             )
-#             tab_interface_ls['Text Ops'] = comp
-#             if mode == 'text':
-#                 return comp
-#         if mode == 'image' or mode == 'mix':
-#             # https://www.gradio.app/guides/the-interface-class
-#             comp = gr.Interface(
-#                 fn= self.test_image,
-#                 inputs=['image', gr.Dropdown(['sepia', 'grayscale'])],
-#                 outputs=["textbox",'image'],
-#                 title="test image preprocess Module",
-#                 description="test text.",
-#                 theme="huggingface",
-#                 examples=[
-#                     ["/mnt/c/Users/wwk/Pictures/OIP.jpg", "sepia"],
-#                 ],
-#             )
-#             tab_interface_ls['Image Ops'] = comp
-#             if mode == 'image':
-#                 return comp
-
-#         if mode == 'audio' or mode == 'mix':
-#             comp = gr.Interface(
-#                 fn= self.test_audio,
-#                 inputs=['audio', gr.Dropdown(['echo', 'reverse']), 'textbox', gr.CheckboxGroup([ 'make_instrumental' ,'wait_audio'],  label="Suno options", info="make_instrumental<bool>, wait_audio:<bool>") ],
-#                 outputs=["textbox", 'audio'],
-#                 title="test audio preprocess Module",
-#                 description="test audio.",
-#                 theme="huggingface",
-#                 examples=[
-#                     ["/mnt/d/workspace/projects/movie_generator/examples/audio/两只老虎，两只老虎-神秘-欢快-v2.mp3", "echo"],
-#                     ["/mnt/d/workspace/projects/movie_generator/examples/audio/两只老虎，两只老虎-神秘-欢快-v2.mp3", "reverse"],
-#                 ],
-#             )
-#             tab_interface_ls['Audio Ops'] = comp
-#             if mode == 'audio':
-#                 return comp
-            
-#         if mode == 'video' or mode == 'mix':
-#             comp = gr.Interface(
-#                 fn= self.test_video,
-#                 inputs= [ 'video', gr.Dropdown(['clip', 'rotate'])],
-#                 outputs=["textbox", 'video'],
-#                 title="test video preprocess Module",
-#                 description="test video.",
-#                 theme="huggingface",
-#                 examples=[
-#                     ["/mnt/d/workspace/projects/movie_generator/examples/video/2月12日.mp4", "clip"],
-#                 ],
-#                 )
-#             tab_interface_ls['Video Ops'] = comp
-#             if mode == 'video':
-#                 return comp
-            
-#         if mode == 'chat' or mode == 'mix':
-#             # https://www.gradio.app/guides/creating-a-custom-chatbot-with-blocks
-#             # comp = gr.ChatInterface(self.test_chatbot)
-#             comp = gr.ChatInterface(self.predict_v2)
-#             tab_interface_ls['ChatBot'] = comp
-#             if mode == 'chat':
-#                 return comp    
-#         if mode == 'mix':
-#             # mix mode, use radio button to select the mode
-#             comp = gr.TabbedInterface(
-#                 list(tab_interface_ls.values()), list(tab_interface_ls.keys())
-#             )
-#             return comp
-#         else:
-#             def flip_text(x):
-#                 return x[::-1]
-#             def flip_image(x):
-#                 return np.fliplr(x)
-#             with gr.Blocks() as comp:
-#                 gr.Markdown("Flip text or image files using this demo.")
-#                 with gr.Tab("Flip Text"):
-#                     text_input = gr.Textbox()
-#                     text_output = gr.Textbox()
-#                     text_button = gr.Button("Flip")
-#                 with gr.Tab("Flip Image"):
-#                     with gr.Row():
-#                         image_input = gr.Image()
-#                         image_output = gr.Image()
-#                     image_button = gr.Button("Flip")
-
-#                 with gr.Accordion("Open for More!", open=False):
-#                     gr.Markdown("Look at me...")
-#                     temp_slider = gr.Slider(
-#                         minimum=0.0,
-#                         maximum=1.0,
-#                         value=0.1,
-#                         step=0.1,
-#                         interactive=True,
-#                         label="Slide me",
-#                     )
-#                     temp_slider.change(lambda x: x, [temp_slider])
-
-#                 text_button.click(flip_text, inputs=text_input, outputs=text_output)
-#                 image_button.click(flip_image, inputs=image_input, outputs=image_output)
-#         return comp
+import gradio as gr
+
+with gr.Blocks(fill_height=True) as demo:
+    with gr.Sidebar():
+        gr.Markdown("# Inference Provider")
+        gr.Markdown("This Space showcases the black-forest-labs/FLUX.1-dev model, served by the nebius API. Sign in with your Hugging Face account to use this API.")
+        button = gr.LoginButton("Sign in")
+    gr.load("models/black-forest-labs/FLUX.1-dev", accept_token=button, provider="nebius")
+    
+demo.launch()

appv2.py

@@ -0,0 +1,588 @@
+# Dependencies: gradio, fire, langchain, openai, numpy, ffmpeg, moviepy
+# API Reference: https://www.gradio.app/docs/,
+# https://github.com/zhayujie/chatgpt-on-wechat, https://docs.link-ai.tech/platform/api,  https://docs.link-ai.tech/api#/
+# Description: This file contains the code to run the gradio app for the movie generator.
+# 
+#
+#
+# 参考链接： https://zhuanlan.zhihu.com/p/684798694
+#
+#
+####################################################################################################
+
+import gradio as gr
+#import fire
+from gradio_client import Client, file
+import numpy as np
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import AIMessage, HumanMessage
+
+from openai import OpenAI
+import os
+import moviepy.editor as mppyth
+from moviepy.editor import *
+# from movie_generator.agi.suno.suno import Suno
+import requests
+
+
+import ollama
+from ollama import chat
+from ollama import ChatResponse
+
+# ollama.pull("deepseek-r1:1.5b")
+# print( 'ollama result:',ollama.list())
+# response: ChatResponse = chat(model='deepseek-r1:1.5b', messages=[
+#   {
+#     'role': 'user',
+#     'content': 'Why is the sky blue?',
+#   },
+# ])
+# print(response['message']['content'])
+# # or access fields directly from the response object
+# print(response.message.content)
+
+def call_LLM(inputs, prompts= '你是一个时尚服装行业的专家， 请回答下面问题：', model_version = 'Qwen'):
+    inputs = prompts + ' ' + inputs
+    if model_version=="Qwen":
+        from openai import OpenAI
+
+        model_id = 'Qwen/Qwen2.5-3B-Instruct-GGUF'
+
+        client = OpenAI(
+            base_url='https://ms-fc-2ea3820b-8c19.api-inference.modelscope.cn/v1',
+            api_key='e37bfdad-0f6a-46c2-a7bf-f9dc365967e3'
+        )
+
+        response=client.chat.completions.create(
+            model=model_id,
+            messages=[{"role":"user", "content":inputs}],
+            stream=True
+        )
+
+        res= []
+        for chunk in response:
+            # print(chunk.choices[0].delta.content, end='', flush=True)
+            res.append(chunk.choices[0].delta.content)
+        return "".join(res)
+    elif model_version in ['deepseek-r1:1.5b', 'llama3.2:latest']: 
+        
+        # model= 'deepseek-r1:1.5b'
+        # model = 'llama3.2:latest'
+        response: ChatResponse = chat(model= model_version, messages=[
+        {
+            'role': 'user',
+            'content': prompts + " " + inputs,
+        },
+        ])
+        return response['message']['content']
+    else:
+        return "LLM version is not supported yet."
+import os
+class GradioApp:
+    def __init__(self,config=None):
+        #config with info of 
+        # model version
+        # prompts
+        #others
+        self.config=config
+        # self.image_dir = "/mnt/d/workspace/projects/Project_TextImage_Generator/examples"
+        self.image_dir = "../examples"
+        self.model_dir = os.path.join(self.image_dir, "models")
+        self.clothes_dir = os.path.join(self.image_dir, "clothes")
+        self.reference_dir = os.path.join(self.image_dir, "references")
+        self.model_files = [os.path.join(self.model_dir, f) for f in os.listdir(self.model_dir)]
+        self.clothes_files = [os.path.join(self.clothes_dir, f) for f in os.listdir(self.clothes_dir)]
+        self.reference_files = [os.path.join(self.reference_dir, f) for f in os.listdir(self.reference_dir)]
+        pass
+    
+    
+    def test_image_func(self, input_image, filter_mode='sepia'):
+        def filter_image(input_image, filter_mode='sepia'):
+            def sepia(input_img):
+                sepia_filter = np.array([
+                    [0.393, 0.769, 0.189], 
+                    [0.349, 0.686, 0.168], 
+                    [0.272, 0.534, 0.131]
+                ])
+                sepia_img = input_img.dot(sepia_filter.T)
+                sepia_img /= sepia_img.max()
+                return sepia_img
+            def grayscale(input_img):
+                input_img = np.mean(input_img, axis=2) / np.max(input_img)
+                return input_img
+            if filter_mode == 'sepia':
+                return sepia(input_image)
+            elif filter_mode == 'grayscale':
+                return grayscale(input_image)
+            else:
+                return input_image
+        res = f"Got image from image input: {input_image}"
+        filtered_image = filter_image(input_image, filter_mode)
+        return res, filtered_image
+    
+    def dress_up_func(self, model_images, cloths_images, prompts, similarity):
+        # 请求GPT response
+        return "dress_up_func output",[(model_images, "模特"), (cloths_images, "衣服")]*5
+
+    def update_model_func(self, model_images, cloths_images, prompts, similarity):
+        # 请求GPT response
+        return "update_model_func output", [(model_images, "模特"), (cloths_images, "衣服")]*5
+    
+    def image_module(self, mode='dress_up', title='image_module', desc=''):
+        if mode == 'dress_up':
+            # 模特试衣
+            func = self.dress_up_func
+        elif mode == 'update_model':
+            # 更新模特
+            func = self.update_model_func
+        else:
+            func = self.dress_up_func
+        examples = []
+        for i, (c, m) in enumerate( zip(self.clothes_files, self.model_files) ):
+            examples.append([c, m, 'sepia', 0.6] )
+        comp = gr.Interface(
+                fn= func,
+                inputs=[gr.Image(label='衣服', scale=1, height=300),
+                        gr.Image(label='模特',scale=1, height=300),
+                        gr.Dropdown(['sepia', 'grayscale']),
+                        gr.Slider(0, 10, value=5, label="相似度控制", info="similarity between 2 and 20")],
+                outputs=[gr.Textbox(label="文本输出"),
+                         gr.Gallery(label='图片展示',height='auto',columns=3)
+                         ],
+                title=title,
+                description=desc,
+                theme="huggingface",
+                examples=examples,
+            )
+        return comp
+    
+    def image_module_v2(self, mode='dress_up', title='image_module', desc=''):
+        def upload_file(files, current_files):
+            file_paths = current_files + [file.name for file in files]
+            return file_paths
+
+        def gen_images(clothes_img, model_img):
+            new_images = []
+            #call LLM/SD here
+            new_images.append(clothes_img)
+            new_images.append(model_img)
+            return new_images
+        
+        def clear_images():
+            return []
+        def slider_func(val):
+            print("slider value: ", val)
+
+
+        if mode == 'dress_up':
+            # 模特试衣
+            func = self.dress_up_func
+        elif mode == 'update_model':
+            # 更新模特
+            func = self.update_model_func
+        else:
+            func = self.dress_up_func
+
+        with gr.Blocks() as demo:
+            # first row
+            with gr.Row():
+                # first col -> input column
+                with gr.Column():
+                    model_image=gr.Image(label="模特图片",type='pil', height=None, width=None)
+                    clothes_image=gr.Image(label="衣服图片",type='pil', height=None, width=None)
+                    upload_button = gr.UploadButton("选择图片上传 (Upload Photos)", file_types=["image"], file_count="multiple")
+                    generate_img_button = gr.Button("生成图片")
+                    slider = gr.Slider(0, 10, value=5, label="相似度控制", info="similarity between 2 and 20")
+                    clear_button = gr.Button("清空图片 (Clear Photos)")
+                    
+                    # analyze_button = gr.Button("显示图片信息 (Show Image Info)")
+                    input_image_gallery = gr.Gallery(type='pil', label='输入图片列表 (Photos)', height=250, columns=4, visible=True)
+                # second col-> output column
+                with gr.Column():
+                    image_gallery = gr.Gallery(type='pil', label='图片列表 (Photos)', height=250, columns=4, visible=True)
+            # user_images = gr.State([])
+            # upload_button.upload(upload_file, inputs=[upload_button, user_images], outputs=image_gallery)
+            slider.input(fn=slider_func)
+            generate_img_button.click(gen_images,inputs=[clothes_image, model_image], outputs= image_gallery)
+            clear_button.click(fn=clear_images, inputs=None, outputs=image_gallery)
+            # analyze_button.click(get_image_info, inputs=image_gallery, outputs=analysis_output)
+            return demo
+
+    def gen_text(self,inputs, LLM_version='Qwen'):
+        # 设置前置prompt做限制
+        prompts = "你是一个时尚服装行业的专家， 请回答下面问题,只罗列答案不要返回多余的词："
+        # model= 'deepseek-r1:1.5b'
+        # return call_LLM(inputs,prompts, model_version='llama3.2:latest')
+        return call_LLM(inputs,prompts, model_version=LLM_version)
+    
+    def text_module(self, title='文本生成', desc="AI生成关键词"):
+        comp = gr.Interface(
+                fn= self.gen_text,
+                inputs=[gr.Textbox(label="文本输入"), gr.Dropdown(['deepseek-r1:1.5b', 'llama3.2:latest','Qwen'], label='模型选择')],
+                outputs=[gr.Textbox(label="结果输出")],
+                title=title,
+                description=desc,
+                theme="huggingface",
+                examples=[
+                    ["列出2024年最受欢迎的10个衣服品牌","llama3.2:latest"],
+                      ["哪些款式的女装比较潮流， 请列出10个女装品类","Qwen"],
+                      ["随机生成10个衣服类目并列出来","Qwen"]],
+                cache_examples=True,
+            )
+        return comp
+    
+    def generate_interface(self,):
+        tab_interface_ls = {}
+        # module 1: 生词
+        tab_interface_ls['AI生词'] = self.text_module()
+
+        # module 2: 服装上身
+        tab_interface_ls['服装搭配'] = self.image_module('dress_up', title="服装搭配")
+           
+        # module 3: 换模特
+        tab_interface_ls['更换模特'] = self.image_module('update_model', title="更换模特")
+
+        comp = gr.TabbedInterface(
+                list(tab_interface_ls.values()), list(tab_interface_ls.keys())
+            )
+        return comp
+
+def main():
+    print(f"Runing Gradio APP")
+    component = GradioApp()
+    component.generate_interface().launch(share=True)
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+# class GradioUnitTest():
+#     def __init__(self):
+#         api_key =  "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"
+#         # "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"
+#         os.environ["OPENAI_API_KEY"] = api_key
+#         self.llm_model = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
+#         # self.llm_model= None
+#         self.client = OpenAI(api_key=api_key)
+#         cur_path =os.getcwd()
+#         root_path = '/'.join(cur_path.split("/")[:-2])
+
+#         suno_result_path = os.path.join(root_path,'examples','suno_musics')
+#         # self.suno = Suno(result_path=suno_result_path)
+#         self.suno= None
+#         pass
+    
+#     def test_text(self, input_text, mode = 'count'):
+#         def process_test( _text, mode = 'count'):
+#             def count_words(text):
+#                 words = text.split(" ")
+#                 res_dict = {}
+#                 for word in words:
+#                     if word in res_dict:
+#                         res_dict[word] += 1
+#                     else:
+#                         res_dict[word] = 1
+#                 res = "\n".join([f"word: {key}, count: {value}" for key, value in res_dict.items()])
+#                 return res
+            
+#             def reverse_text(text):
+#                 return text[::-1]
+            
+#             if mode == 'count':
+#                 return count_words(_text)
+#             return reverse_text(_text)
+
+#         res = f"Got text from textbox: {input_text}"
+#         return res, process_test(input_text, mode)
+#         # return res, count_words(input_text)
+    
+#     def test_image(self, input_image, filter_mode='sepia'):
+#         def filter_image(input_image, filter_mode='sepia'):
+#             def sepia(input_img):
+#                 sepia_filter = np.array([
+#                     [0.393, 0.769, 0.189], 
+#                     [0.349, 0.686, 0.168], 
+#                     [0.272, 0.534, 0.131]
+#                 ])
+#                 sepia_img = input_img.dot(sepia_filter.T)
+#                 sepia_img /= sepia_img.max()
+#                 return sepia_img
+#             def grayscale(input_img):
+#                 input_img = np.mean(input_img, axis=2) / np.max(input_img)
+#                 return input_img
+            
+#             if filter_mode == 'sepia':
+#                 return sepia(input_image)
+#             elif filter_mode == 'grayscale':
+#                 return grayscale(input_image)
+#             else:
+#                 return input_image
+#         res = f"Got image from image input: {input_image}"
+#         filtered_image = filter_image(input_image, filter_mode)
+#         return res, filtered_image
+    
+#     def test_audio(self, input_audio, filter_mode='echo', prompt='', checkbox_ls=[]):
+#         def process_audio(input_audio, filter_mode='echo'):
+#             print("input_audio shape: ", input_audio[1].shape, input_audio)
+#             def echo(input_audio):
+#                 aud = np.concatenate([input_audio[1], input_audio[1]], axis=0)
+#                 return (input_audio[0], aud)
+#             def reverse(input_audio):
+#                 return (input_audio[0], input_audio[1][::-1]) 
+            
+#             if filter_mode == 'echo':
+#                 res_audio = echo(input_audio)
+#             elif filter_mode == 'reverse':
+#                 res_audio = reverse(input_audio)
+#             else:
+#                 res_audio = input_audio
+#             return res_audio
+#         print("checkbox_ls: ", checkbox_ls)
+#         res = f"Got audio from audio input: {input_audio}"
+#         wait_audio = 'wait_audio' in checkbox_ls
+#         make_instrumental = 'make_instrumental' in checkbox_ls
+#         if checkbox_ls != []:
+#             print('checlbox_ls: ', checkbox_ls)
+#         generated_audio_path=''
+#         if prompt != '':
+#             music_paths = self.test_music_generation(prompt, make_instrumental, wait_audio)
+#             generated_audio_path = '\n'.join(music_paths)
+#             res = f"Got audio from suno: {generated_audio_path}"
+#         processed_audio = process_audio(input_audio, filter_mode)
+#         return res, processed_audio, generated_audio_path
+
+#     def test_video(self, input_video, filter_mode='flip'):
+#         def process_video(input_video, filter_mode='flip'):
+#             print("input_video data: ", input_video)
+
+#             def clip(input_video):
+#                 clip1 = VideoFileClip(input_video)
+#                 clip2 = VideoFileClip(input_video).subclip(2,3)
+#                 clip3 = VideoFileClip(input_video)
+#                 final_clip = concatenate_videoclips([clip1,clip2,clip3])
+#                 output_video = "final_clip.mp4"
+#                 final_clip.write_videofile(output_video)
+#                 return output_video
+#             def flip(input_video):
+#                 return np.flip(input_video, axis=1)
+#             def rotate(input_video):
+#                 return np.rot90(input_video)
+#             if filter_mode == 'clip':
+#                 return clip(input_video)
+#             elif filter_mode == 'flip':
+#                 return flip(input_video)
+#             elif filter_mode == 'rotate':
+#                 return rotate(input_video)
+#             else:
+#                 return input_video
+#         res = f"Got video from video input: {input_video}"
+#         processed_video = process_video(input_video, filter_mode)
+#         return res, processed_video
+
+#     def test_chatbot(self, input_text, history):
+#         history_langchain_format =[]
+#         for human, ai in history:
+#             history_langchain_format.append(HumanMessage(human))
+#             history_langchain_format.append(AIMessage(ai))
+#         history_langchain_format.append(content=input_text)
+#         llm_response = self.llm_model(history_langchain_format)
+#         return llm_response.content
+
+#     def predict(self, message, history):
+#         history_openai_format = []
+#         for human, assistant in history:
+#             history_openai_format.append({"role": "user", "content": human })
+#             history_openai_format.append({"role": "assistant", "content":assistant})
+#         history_openai_format.append({"role": "user", "content": message})
+    
+#         response = self.client.chat.completions.create(model='gpt-3.5-turbo',
+#         messages= history_openai_format,
+#         temperature=1.0,
+#         stream=True)
+
+#         partial_message = ""
+#         for chunk in response:
+#             if chunk.choices[0].delta.content is not None:
+#                 partial_message = partial_message + chunk.choices[0].delta.content
+#                 yield partial_message
+    
+#     def predict_v2(self, message, history):
+        
+#         url = "https://api.link-ai.chat/v1/chat/completions"
+#         headers = {
+#             'Authorization': 'Bearer Link_USN4Vru40ciqYkdpeWywmOOIOPHGLYm8EuAGm0xE0b',
+#             'Content-Type': 'application/json'
+#         }
+#         history_openai_format = []
+#         for human, assistant in history:
+#             history_openai_format.append({"role": "user", "content": human })
+#             history_openai_format.append({"role": "assistant", "content":assistant})
+#         history_openai_format.append({"role": "user", "content": message})
+    
+
+#         data = {
+#             "app_code": "default",
+#             "messages": history_openai_format,
+#         }
+
+#         response = requests.post(url, headers=headers, json=data).json()
+#         partial_message = ""
+#         for chunk in response['choices']:
+#             if chunk['message']["content"] is not None:
+#                 partial_message = partial_message + chunk['message']["content"]
+#                 yield partial_message
+    
+
+#     def predict_v3(self, message, history):
+        
+#         url = "https://api.link-ai.chat/v1/chat/completions"
+#         headers = {
+#             'Authorization': 'Bearer Link_USN4Vru40ciqYkdpeWywmOOIOPHGLYm8EuAGm0xE0b',
+#             'Content-Type': 'application/json'
+#         }
+#         history_openai_format = []
+#         for human, assistant in history:
+#             history_openai_format.append({"role": "user", "content": human })
+#             history_openai_format.append({"role": "assistant", "content":assistant})
+#         history_openai_format.append({"role": "user", "content": message})
+    
+
+#         data = {
+#             "app_code": "default",
+#             "messages": history_openai_format,
+#         }
+
+#         response = requests.post(url, headers=headers, json=data).json()
+#         partial_message = ""
+#         for chunk in response['choices']:
+#             if chunk['message']["content"] is not None:
+#                 partial_message = partial_message + chunk['message']["content"]
+#                 yield partial_message
+
+#     def test_music_generation(self, prompt, make_instrumental=False, wait_audio=False):
+#         request = {
+#             "prompt": prompt,
+#             "make_instrumental": make_instrumental,
+#             "wait_audio": wait_audio
+#             }
+#         # music_ls = self.suno.generate_music(request)
+#         music_ls = []
+#         return music_ls
+
+#     def run_test(self, mode='text'):
+#         tab_interface_ls = {}
+#         if mode == 'text' or mode == 'mix':
+#             comp = gr.Interface(
+#                 fn= self.test_text,
+#                 inputs=['textbox', gr.Dropdown(['count', 'reverse'])],
+#                 outputs=["textbox", "textbox"],
+#                 title="test text module",
+#                 description="test text.",
+#                 theme="huggingface",
+#                 examples=[
+#                     ["A group of friends go on a road trip to find a hidden treasure."],
+#                     ["A scientist discovers a way to travel through time."],
+#                     ["A group of survivors try to escape a zombie apocalypse."],
+#                 ],
+#             )
+#             tab_interface_ls['Text Ops'] = comp
+#             if mode == 'text':
+#                 return comp
+#         if mode == 'image' or mode == 'mix':
+#             # https://www.gradio.app/guides/the-interface-class
+#             comp = gr.Interface(
+#                 fn= self.test_image,
+#                 inputs=['image', gr.Dropdown(['sepia', 'grayscale'])],
+#                 outputs=["textbox",'image'],
+#                 title="test image preprocess Module",
+#                 description="test text.",
+#                 theme="huggingface",
+#                 examples=[
+#                     ["/mnt/c/Users/wwk/Pictures/OIP.jpg", "sepia"],
+#                 ],
+#             )
+#             tab_interface_ls['Image Ops'] = comp
+#             if mode == 'image':
+#                 return comp
+
+#         if mode == 'audio' or mode == 'mix':
+#             comp = gr.Interface(
+#                 fn= self.test_audio,
+#                 inputs=['audio', gr.Dropdown(['echo', 'reverse']), 'textbox', gr.CheckboxGroup([ 'make_instrumental' ,'wait_audio'],  label="Suno options", info="make_instrumental<bool>, wait_audio:<bool>") ],
+#                 outputs=["textbox", 'audio'],
+#                 title="test audio preprocess Module",
+#                 description="test audio.",
+#                 theme="huggingface",
+#                 examples=[
+#                     ["/mnt/d/workspace/projects/movie_generator/examples/audio/两只老虎，两只老虎-神秘-欢快-v2.mp3", "echo"],
+#                     ["/mnt/d/workspace/projects/movie_generator/examples/audio/两只老虎，两只老虎-神秘-欢快-v2.mp3", "reverse"],
+#                 ],
+#             )
+#             tab_interface_ls['Audio Ops'] = comp
+#             if mode == 'audio':
+#                 return comp
+            
+#         if mode == 'video' or mode == 'mix':
+#             comp = gr.Interface(
+#                 fn= self.test_video,
+#                 inputs= [ 'video', gr.Dropdown(['clip', 'rotate'])],
+#                 outputs=["textbox", 'video'],
+#                 title="test video preprocess Module",
+#                 description="test video.",
+#                 theme="huggingface",
+#                 examples=[
+#                     ["/mnt/d/workspace/projects/movie_generator/examples/video/2月12日.mp4", "clip"],
+#                 ],
+#                 )
+#             tab_interface_ls['Video Ops'] = comp
+#             if mode == 'video':
+#                 return comp
+            
+#         if mode == 'chat' or mode == 'mix':
+#             # https://www.gradio.app/guides/creating-a-custom-chatbot-with-blocks
+#             # comp = gr.ChatInterface(self.test_chatbot)
+#             comp = gr.ChatInterface(self.predict_v2)
+#             tab_interface_ls['ChatBot'] = comp
+#             if mode == 'chat':
+#                 return comp    
+#         if mode == 'mix':
+#             # mix mode, use radio button to select the mode
+#             comp = gr.TabbedInterface(
+#                 list(tab_interface_ls.values()), list(tab_interface_ls.keys())
+#             )
+#             return comp
+#         else:
+#             def flip_text(x):
+#                 return x[::-1]
+#             def flip_image(x):
+#                 return np.fliplr(x)
+#             with gr.Blocks() as comp:
+#                 gr.Markdown("Flip text or image files using this demo.")
+#                 with gr.Tab("Flip Text"):
+#                     text_input = gr.Textbox()
+#                     text_output = gr.Textbox()
+#                     text_button = gr.Button("Flip")
+#                 with gr.Tab("Flip Image"):
+#                     with gr.Row():
+#                         image_input = gr.Image()
+#                         image_output = gr.Image()
+#                     image_button = gr.Button("Flip")
+
+#                 with gr.Accordion("Open for More!", open=False):
+#                     gr.Markdown("Look at me...")
+#                     temp_slider = gr.Slider(
+#                         minimum=0.0,
+#                         maximum=1.0,
+#                         value=0.1,
+#                         step=0.1,
+#                         interactive=True,
+#                         label="Slide me",
+#                     )
+#                     temp_slider.change(lambda x: x, [temp_slider])
+
+#                 text_button.click(flip_text, inputs=text_input, outputs=text_output)
+#                 image_button.click(flip_image, inputs=image_input, outputs=image_output)
+#         return comp