import numpy as np import gradio as gr import torch import requests from PIL import Image from diffusers import StableDiffusionDepth2ImgPipeline from PIL import Image import time import io import os import warnings from PIL import Image from stability_sdk import client import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation from diffusers import StableDiffusionImg2ImgPipeline import urllib from serpapi import GoogleSearch from base64 import b64encode from pathlib import Path import openai import logging import grpc import matplotlib.pyplot as plt try: import face_recognition except: pass import pickle import numpy as np from PIL import Image import cv2 logging.basicConfig(level=logging.DEBUG,filename="logger.log",filemode="a") print("Hello") current_time = time.asctime() stability_api = client.StabilityInference( key=os.environ['STABILITY_KEY'], # API Key reference. verbose=True, # Print debug messages. engine="stable-diffusion-512-v2-1", # Set the engine to use for generation. For SD 2.0 use "stable-diffusion-v2-0". # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0 # stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-inpainting-v1-0 stable-inpainting-512-v2-0 ) ################ # Set up our initial generation parameters. prompt ="photo of bespectacled woman, long curly blue hair, bright green eyes, freckled complexion, photorealistic, colorful, highly detailed 4k, realistic photo" def transform_ncuda(img,prompt,cfg=8.0,stps=30,sc=0.8): answers2 = stability_api.generate( prompt=f"{prompt}", init_image=img, # Assign our previously generated img as our Initial Image for transformation. start_schedule=sc, # Set the strength of our prompt in relation to our initial image. steps=stps,# If attempting to transform an image that was previously generated with our API, # initial images benefit from having their own distinct seed rather than using the seed of the original image generation. # Amount of inference steps performed on image generation. Defaults to 30. cfg_scale=cfg, # Influences how strongly your generation is guided to match your prompt. # Setting this value higher increases the strength in which it tries to match your prompt. # Defaults to 7.0 if not specified. width=512, # Generation width, defaults to 512 if not included. height=512, # Generation height, defaults to 512 if not included. sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with. # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers. # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m) ) # Set up our warning to print to the console if the adult content classifier is tripped. # If adult content classifier is not tripped, display generated image. try: for resp in answers2: print('----------------------------------------------------------------------------------') print(f'{resp}') print(f'DEBUG: Type = {resp.__class__}') for artifact in resp.artifacts: if artifact.finish_reason == generation.FILTER: warnings.warn( "Your request activated the API's safety filters and could not be processed." "Please modify the prompt and try again.") if artifact.type == generation.ARTIFACT_IMAGE: global img2 img2 = Image.open(io.BytesIO(artifact.binary)) return img2 except Exception as e: img = img.resize((832,832), Image.ANTIALIAS) return transform_ncuda(img,prompt,cfg=8.0,stps=30,sc=0.8) # print(f'Caught error: {e}') # logging.warn(f'Caught error: {e}') # img = img.resize((256,256), Image.ANTIALIAS) # print(f'Image resizing: (256,256)') # return transform_ncuda(img,prompt) # img2.save(str(artifact.seed)+ "-img2img.png") # Save our generated image with its seed number as the filename and the img2img suffix so that we know this is our transformed image. ######################### def generate_stability(prompt): # Set up our initial generation parameters. answers = stability_api.generate( prompt=f"{prompt}", # If a seed is provided, the resulting generated image will be deterministic. # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again. # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook. steps=30, # Amount of inference steps performed on image generation. Defaults to 30. cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt. # Setting this value higher increases the strength in which it tries to match your prompt. # Defaults to 7.0 if not specified. width=512, # Generation width, defaults to 512 if not included. height=512, # Generation height, defaults to 512 if not included. samples=1, # Number of images to generate, defaults to 1 if not included. sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with. # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers. # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m) ) # Set up our warning to print to the console if the adult content classifier is tripped. # If adult content classifier is not tripped, save generated images. for resp in answers: for artifact in resp.artifacts: if artifact.finish_reason == generation.FILTER: warnings.warn( "Your request activated the API's safety filters and could not be processed." "Please modify the prompt and try again.") if artifact.type == generation.ARTIFACT_IMAGE: img = Image.open(io.BytesIO(artifact.binary)) # img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename. return img ################# global cuda_error1 cuda_error1 = 0 try: device = "cuda" model_id_or_path = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) pipe = pipe.to(device) except: cuda_error1 = 1 ##################### global cuda_error2 cuda_error2 = 0 try: pipe1 = StableDiffusionDepth2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-2-depth", torch_dtype=torch.float16, ).to("cuda") except: cuda_error2 = 1 ################## def transform(init_image,prompt,n_prompt): # init_image = init_image.resize((256,256), Image.ANTIALIAS) if cuda_error2==0: try: image1 = pipe1(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.8).images[0] except: image1 = transform_ncuda(init_image,prompt) # image1.save("img1.png") # nimage = Image.open("img1.png") else: image1 = transform_ncuda(init_image,prompt) im = np.asarray(image1) return im ################### def transform1(img,prompt,n_prompt): img.save("img1.png") # nimage = Image.open("img1.png").convert('RGB') if cuda_error1==0: try: images = pipe(prompt=prompt, image=nimage,negative_prompt=n_prompt, strength=1, guidance_scale=15).images im = np.asarray(images[0]) except: image = transform_ncuda(img,prompt,15,50,0.95) im = np.asarray(image) # image1.save("img1.png") # nimage = Image.open("img1.png") else: image = transform_ncuda(img,prompt,15,50,0.95) im = np.asarray(image) return im ##################### openai.api_key = os.environ['OPENAI_KEY'] PROMPT = "colorful portrait 25 year bespectacled woman with long, curly skyblue hair and bright green eyes. She has a small, upturned nose and a freckled complexion. She is approximately 5'5 tall and has a thin build" def generate(PROMPT,model): # PROMPT = "An eco-friendly computer from the 90s in the style of vaporwave""Dall-E","StableDiffusion" try: img = generate_stability(PROMPT) except grpc._channel._MultiThreadedRendezvous: raise gr.Error("Invalid prompts detected") return np.asarray(img) ######################## API_ENDPOINT = "https://api.imgbb.com/1/upload" API_KEY = os.environ['IMAGE_API_KEY'] def imgLink(image): pil_image = image.convert('RGB') open_cv_image = np.array(pil_image) cv2.imwrite("search.png",open_cv_image) path = Path("search.png") with open(path, "rb") as image: image_data = b64encode(image.read()).decode() # image_data = image payload = { "key": API_KEY, "image": image_data } # Send the API request response = requests.post(API_ENDPOINT, payload) # print(response) # # Get the generated link from the API response response_json = response.json() # # print("Response json:", response_json) image_url = response_json["data"]["url"] # print("Generated link:", image_url) return image_url ############################ def google_search(image): image_url = imgLink(image) params = { "engine": "google_lens", "url": image_url, "hl": "en", "api_key": os.environ['GOOGLE_SEARCH_API_KEY'] } search = GoogleSearch(params) result = search.get_dict() t = '' try: for i in range(len(result['knowledge_graph'])): t = t+ "Title : "+result['knowledge_graph'][i]['title']+"\n" source = result["knowledge_graph"][i]['images'][0]['source'] t+=source+"\n" except: t = "Not Found" try: for i in range(0,min(2,len(result['visual_matches']))): t = t+ "Title : "+result['visual_matches'][i]['title']+"\n" source = result['visual_matches'][i]['source'] t+=source+"\n" except: t = "Not Found" try: img_link = result["visual_matches"][0]['thumbnail'] urllib.request.urlretrieve(img_link,"file") img = Image.open("file") img = np.asarray(img) except: img = image return t,img ###################################################################### images_folder_path = 'Images' #find path of xml file containing haarcascade file # cascPathface = os.path.dirname( # cv2.__file__) + "/data/haarcascade_frontalface_default.xml" cascPathface = "haarcascade_frontalface_default.xml" # cascPathface = cv2.data.haarcascades + "haarcascade_frontalface_default.xml" # load the harcaascade in the cascade classifier faceCascade = cv2.CascadeClassifier(cascPathface) # load the known faces and embeddings saved in last file data = pickle.loads(open('face_enc', "rb").read()) ################################################################ def check_database(ima): # file_bytes = np.asarray(bytearray(image_upload.read()), dtype=np.uint8) # https://github.com/streamlit/streamlit/issues/888 # opencv_image = cv2.imdecode(file_bytes, 1) # st.image(image, caption=f"Uploaded Image {img_array.shape[0:2]}", use_column_width=True,) # image = cv2.imread(img) # rgb = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB) #convert image to Greyscale for haarcascade # image = cv2.imread(image) try: pil_image = ima.convert('RGB') # pil_image = ima open_cv_image = np.array(pil_image) cv2.imwrite("new.png",open_cv_image) # Convert RGB to BGR image = open_cv_image[:, :, ::-1].copy() gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) faces = faceCascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(60, 60), flags=cv2.CASCADE_SCALE_IMAGE) # the facial embeddings for face in input encodings = face_recognition.face_encodings(image) names = [] # loop over the facial embeddings incase # we have multiple embeddings for multiple fcaes for encoding in encodings: #Compare encodings with encodings in data["encodings"] #Matches contain array with boolean values and True for the embeddings it matches closely #and False for rest matches = face_recognition.compare_faces(data["encodings"], encoding) #set name =inknown if no encoding matches name = "Unknown" # check to see if we have found a match if True in matches: #Find positions at which we get True and store them matchedIdxs = [i for (i, b) in enumerate(matches) if b] counts = {} # loop over the matched indexes and maintain a count for # each recognized face face for i in matchedIdxs: #Check the names at respective indexes we stored in matchedIdxs name = data["names"][i] #increase count for the name we got counts[name] = counts.get(name, 0) + 1 #set name which has highest count name = max(counts, key=counts.get) # update the list of names names.append(name) # loop over the recognized faces for ((x, y, w, h), name) in zip(faces, names): # rescale the face coordinates # draw the predicted face name on the image cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2) else: # To store the unknown new face with name faces = faceCascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(60, 60), flags=cv2.CASCADE_SCALE_IMAGE) cv2.imwrite('curr.png',image) return name except: return "Need GPU" ########################### def video(vid): # return f'Uploaded video name: {vid.name}' file = vid.name print(f'file: {file}') # file = vid video = cv2.VideoCapture(file) # video.set(cv2.CAP_PROP_FPS, 10) if (video.isOpened() == False): print("Error reading video file") frame_width = int(video.get(3)) frame_height = int(video.get(4)) size = (frame_width, frame_height) # # Below VideoWriter object will create # # a frame of above defined The output # # is stored in 'filename.avi' file. result = cv2.VideoWriter('filename.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 10, size) while(True): ret, frame = video.read() if ret == True: rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) faces = faceCascade.detectMultiScale(rgb, scaleFactor=1.1, minNeighbors=5, minSize=(60, 60), flags=cv2.CASCADE_SCALE_IMAGE) # convert the input frame from BGR to RGB # the facial embeddings for face in input encodings = face_recognition.face_encodings(rgb) names = [] # loop over the facial embeddings incase # we have multiple embeddings for multiple fcaes for encoding in encodings: #Compare encodings with encodings in data["encodings"] #Matches contain array with boolean values and True for the embeddings it matches closely #and False for rest matches = face_recognition.compare_faces(data["encodings"], encoding) #set name =inknown if no encoding matches name = "Unknown" # check to see if we have found a match if True in matches: #Find positions at which we get True and store them matchedIdxs = [i for (i, b) in enumerate(matches) if b] counts = {} # loop over the matched indexes and maintain a count for # each recognized face face for i in matchedIdxs: #Check the names at respective indexes we stored in matchedIdxs name = data["names"][i] #increase count for the name we got counts[name] = counts.get(name, 0) + 1 #set name which has highest count name = max(counts, key=counts.get) # update the list of names names.append(name) # loop over the recognized faces for ((x, y, w, h), name) in zip(faces, names): # rescale the face coordinates # draw the predicted face name on the image cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2) result.write(frame) # cv2_imshow(frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # Break the loop else: break # print("The video was successfully saved") return 'filename.mp4' ################# def generate_prompt(AG,facftop,facfmid,facfbot): response = openai.Completion.create( model="text-davinci-003", prompt="Generate Facial Description of person from the following desciptors-Realistic facial portrait sketch of " + AG + facftop + facfmid + facfbot, temperature=0.1, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) return (response["choices"][0]["text"]) ############################## openai.api_key = os.environ['OPENAI_KEY'] # os.getenv() PROMPT = "Ankit went to the market. He called Raj then." response = openai.Completion.create( model="text-davinci-003", prompt=f"Given a prompt, extrapolate as many relationships as possible from it and provide a list of updates.\n\nIf an update is a relationship, provide [ENTITY 1, RELATIONSHIP, ENTITY 2]. The relationship is directed, so the order matters.\n\nIf an update is related to deleting an entity, provide [\"DELETE\", ENTITY].\n\nExample:\nprompt: Alice is Bob's roommate. Alice likes music. Her roommate likes sports\nupdates:\n[[\"Alice\", \"roommate\", \"Bob\"],[\"Alice\",\"likes\",\"music\"],[\"Bob\",\"likes\",\"sports\"]]\n\nprompt: {PROMPT}\nupdates:", temperature=0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) ################### t = response["choices"][0]["text"] t = t[2:] t = t.replace("[",'').replace("]","") t = t.split(",") r = [] for i in range(len(t)//3): r.append(t[3*i:3*i+3]) r def get_edge_labels(t:list): dct = {} length_of_t = len(t) for i in range(length_of_t): t[i][0] = t[i][0].replace('"',"").replace("'","").strip() t[i][2] = t[i][2].replace('"',"").replace("'","").strip() t[i][1] = t[i][1].replace('"',"").replace("'","") dct[(t[i][0],t[i][2] )] = t[i][1] return dct def knowledge_graph(prompt): response = openai.Completion.create( model="text-davinci-003", prompt=f"""Given a prompt, extrapolate as many relationships as possible from it and provide a list of updates.\n\nIf an update is a relationship, provide [ENTITY 1, RELATIONSHIP, ENTITY 2]. The relationship is directed, so the order matters.\n\nIf an update is related to deleting an entity, provide [\"DELETE\", ENTITY].\n\nExample:\nprompt: Alice is Bob's roommate. Alice likes music. Her roommate likes sports\nupdates:\n[[\"Alice\", \"roommate\", \"Bob\"],[\"Alice\",\"likes\",\"music\"], [\"Bob\",\"likes\",\"sports\"]]\n\nprompt: {prompt}\nupdates:""", temperature=0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) r = response["choices"][0]["text"] r = r[2:] r = r.replace("[",'').replace("]","") r = r.split(",") t = [] for i in range(len(r)//3): t.append(r[3*i:3*i+3]) # t = [['"Ankit"', '"went_to"', '"market"'], ['"Ankit"', '"called"', '"Raj"']] import networkx as nx import random print(t) G = nx.Graph() new_nodes = [] print('Edge labels') edge_labels = get_edge_labels(t) print(edge_labels) print(f't after edge labesl = {t}') for i in t: if not i[0] in new_nodes: new_nodes.append(i[0]) G.add_node(i[0]) if not i[2] in new_nodes: new_nodes.append(i[2]) G.add_node(i[2]) # G.add_node(i[0]) # G.add_node(i[2]) G.add_edge(i[0],i[2]) pos = nx.spring_layout(G) nx.draw(G,pos,labels={node: node for node in G.nodes()}) x = nx.draw_networkx_edge_labels( G, pos, edge_labels=edge_labels, font_color='red' ) # print(x) random_name = f'generated_img_{random.randint(1,100000)}.png' plt.savefig(f"/tmp/{random_name}") plt.clf() img = Image.open(f"/tmp/{random_name}") os.remove(f"/tmp/{random_name}") return np.asarray(img) c =knowledge_graph("Alice went to office. Called bob. Went to grocery shopping. Then went home") ##################### disp_url = "https://i.ibb.co/TP4ddc6/sherlock.png" det_url = "https://i.ibb.co/Ms1jcDv/104cc37752fa.png" with gr.Blocks(css=".gradio-container {background-color: #F0FFFF}") as demo: gr.Markdown("""

Sherlock's Phoeniks

""") gr.Markdown("

Facial Recognition using Generative AI - ChatGPT+StableDiffusion,utilizing Computer Vision and Google Search API

") # gr.Image(display).style(height=400, width=1200) gr.HTML(value="Flow Diagram") # gr.Markdown("! [title](https://pixabay.com/photos/tree-sunset-clouds-sky-silhouette-736885/)") gr.Markdown("""

Our Sherlock's Phoeniks Search Squad solution is a facial recognition system that utilizes generative AI models like ChatGPT and stable diffusion, as well as computer vision techniques, to identify and locate missing persons in real time . The system will take input in the form of text describing the appearance of the missing person, as well as raw images such as sketches, CCTV footage, or blurry photos. The algorithm will then search through internal databases and internet/social media platforms like Facebook and Twitter to find matches and potentially identify the missing person. This system has the potential to significantly aid Police and Investigating agencies in their efforts to locate and bring missing persons home

""") gr.HTML(value="Flow Diagram") # gr.Image(detail).style(height=400, width=1200) with gr.Accordion("Generate Prompt",open=False): gr.Markdown("**Generate Prompt**") print('DEBUG: FIRST WITH') gr.Markdown("**Generate Prompt from the face description for image generation**") with gr.Row(): with gr.Column(): print('DEBUG: SECOND WITH') # seed = gr.Text(label="Input Phrase") text1_1 = gr.Text(label="Enter Possible Age and Gender and Ethnicity for the Person") text1_2 = gr.Text(label="Provide Desciptors for Hair and Eyebrows and Eyes") text1_3 = gr.Text(label="Describe Skin Color, Blemishes, Nose Structure") text1_4 = gr.Text(label="Descibe Facial Shape, build , chin structure in as much detail as possible") print(f'{text1_1=}') print(f'{text1_2=}') print(f'{text1_3=}') print(f'{text1_4=}') with gr.Column(): # seed = gr.Text(label="Input Phrase") text2 = gr.Text(label="Generated Phrase") print(text2,'-------------') gr.Markdown("**Refer to the example below**") gr.HTML(value="Generate Prompt") gr.HTML(value="") abtn = gr.Button("Generate mugshot phrase") abtn.click(generate_prompt, inputs=[text1_1,text1_2,text1_3,text1_4], outputs=text2) with gr.Accordion("Generate MugShot",open=False): gr.Markdown("**Generate MugShot from the input prompt using StableDiffusion**") gr.Markdown("**Use StableDiffusion Image Generation for text to image**") # model = gr.Radio(["StableDiffusion"]) with gr.Row(): with gr.Column(): # seed = gr.Text(label="Input Phrase") text3 = gr.Text(label="Input Phrase") with gr.Column(): # seed = gr.Text(label="Input Phrase") im1 = gr.Image() gr.Markdown("**Refer to the example below**") gr.HTML(value="Genrate image from prompt") gr.HTML(value="") bbtn = gr.Button("Image from description") bbtn.click(generate, inputs=[text3], outputs=im1) with gr.Accordion("Image from Sketch",open=False): gr.Markdown("**Get Enhanced Image from sketch and desired input promt using StableDiffusion**") with gr.Accordion("Pre-drawn Sketch",open=False): gr.Markdown("**Generate Colorful Image from pre drawn sketch**") gr.Markdown("**Use StableDiffusion Depth2Image for Image to Image transformation**") with gr.Row(): with gr.Column(): # seed = gr.Text(label="Input Phrase") text4 = gr.Text(label="Prompt") text5 = gr.Text(label="Negative Prompt") im2 = gr.Image(type="pil") with gr.Column(): # seed = gr.Text(label="Input Phrase") im3 = gr.Image() gr.Markdown("**Refer to the example below**") gr.HTML(value="Generate Image from sketch") gr.HTML(value="") cbtn = gr.Button("Sketch to color") cbtn.click(transform, inputs=[im2,text4,text5], outputs=im3) with gr.Accordion("Draw Sketch",open=False): gr.Markdown("**Draw sketch on your own and give text description of features**") gr.Markdown("**Generate Colorful Image using StableDiffusionImg2ImgPipeline**") with gr.Row(): with gr.Column(): # seed = gr.Text(label="Input Phrase") text6 = gr.Text(label="Prompt") text7 = gr.Text(label="Negative Prompt") # im1 = gr.Image(type="pil",interactive=True) im4 = gr.Sketchpad(shape=(256,256),invert_colors=False,type="pil") with gr.Column(): # seed = gr.Text(label="Input Phrase") im5 = gr.Image() ebtn = gr.Button("Draw Sketch to color") ebtn.click(transform1, inputs=[im4,text6,text7], outputs=im5) with gr.Accordion("Check Database",open=False): gr.Markdown("**Check if the image matches any image in our database using face recognition**") gr.Markdown("**Use Face Recognition, Face Detection and Computer Vision to match images**") with gr.Row(): with gr.Column(): # seed = gr.Text(label="Input Phrase") im6 = gr.Image(type="pil") with gr.Column(): # seed = gr.Text(label="Input Phrase") text8 = gr.Text(label="Identified Name") gr.Markdown("**Refer to the example below**") gr.HTML(value="Check Database") gr.HTML(value="") fbtn = gr.Button("Find the Name") fbtn.click(check_database, inputs=im6, outputs=text8) with gr.Accordion("Search Google",open=False): gr.Markdown("**Check if the image is present on the Internet**") gr.Markdown("**Using Google search api to search the image on Web**") with gr.Row(): with gr.Column(): # seed = gr.Text(label="Input Phrase") im7 = gr.Image(type="pil") with gr.Column(): text9 = gr.Text(label="Identified Title") im8 = gr.Image() gr.Markdown("**Refer to the example below**") gr.HTML(value="Check Google") gr.HTML(value="") gbtn = gr.Button("Find the Name") gbtn.click(google_search, inputs=im7, outputs=[text9,im8]) with gr.Accordion("Search in CCTV footage",open=False): gr.Markdown("**Upload a video to identify missing person in the footage**") gr.Markdown("**This feature need GPU to run**") with gr.Row(): with gr.Column(): fil1 = gr.File(type="file") with gr.Column(): vid2 = gr.Video() # video_name = gr.Text(label="Video Upload") gr.Markdown("**Refer to the example below**") gr.HTML(value="Check cctv") gr.HTML(value="") hbtn = gr.Button("Video") hbtn.click(video, inputs=fil1, outputs=vid2) with gr.Accordion("Generate Knowledge Graph",open=False): gr.Markdown("**Genrate Knowledge Graph**") with gr.Row(): with gr.Column(): prompt_to_generate_graph = gr.Text() with gr.Column(): generated_graph_pic = gr.Image() gr.Markdown("**Refer to the example below on how generated knowledge graph**") gr.HTML(value="Generate knowlwdge graph") gr.HTML(value="") generate_knowledge_graph = gr.Button("Generate Knowledge Graph") generate_knowledge_graph.click(knowledge_graph, inputs=prompt_to_generate_graph, outputs=generated_graph_pic) demo.launch(debug=True)