# 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, wait_audio:") ], # 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