Update app.py

app.py

@@ -1,322 +1,63 @@
 import gradio as gr
-import requests
-import time
-import json
-from contextlib import closing
-from websocket import create_connection
-from deep_translator import GoogleTranslator
-from langdetect import detect
-import os
-from PIL import Image
-import io
-from io import BytesIO
-import base64
-import re
-from mistralai import Mistral
-from gradio_client import Client
-from fake_useragent import UserAgent
-import random
-from theme import theme
-from fastapi import FastAPI
 
-app = FastAPI()
-
-api_key = os.getenv("MISTRAL_API_KEY")
-Mistralclient = Mistral(api_key=api_key)
-
-def encode_image(image_path):
-    """Encode the image to base64."""
-    try:
-        # Open the image file
-        image = Image.open(image_path).convert("RGB")
-
-        # Resize the image to a height of 512 while maintaining the aspect ratio
-        base_height = 512
-        h_percent = (base_height / float(image.size[1]))
-        w_size = int((float(image.size[0]) * float(h_percent)))
-        image = image.resize((w_size, base_height), Image.LANCZOS)
-
-        # Convert the image to a byte stream
-        buffered = BytesIO()
-        image.save(buffered, format="JPEG")
-        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-        return img_str
-    except FileNotFoundError:
-        print(f"Error: The file {image_path} was not found.")
-        return None
-    except Exception as e:  # Add generic exception handling
-        print(f"Error: {e}")
-        return None
-
-def feifeichat(image):
-    try:
-        model = "pixtral-large-2411"
-        # Define the messages for the chat
-        base64_image = encode_image(image)
-        messages = [{
-            "role":
-            "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Please provide a detailed description of this photo"
-                },
-                {
-                    "type": "image_url",
-                    "image_url": f"data:image/jpeg;base64,{base64_image}" 
-                },
-            ],
-            "stream": False,
-        }]
+#----------Start of theme----------
+theme = gr.themes.Ocean(
+    primary_hue="zinc",
+    secondary_hue="slate",
+    neutral_hue="neutral",
+    font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'],
+    font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'],
+).set(
+    #Body Settings
+    body_background_fill='linear-gradient(10deg, *primary_200, *secondary_50)',
+    body_text_color='secondary_600',
+    body_text_color_subdued='*primary_500',
+    body_text_weight='500',
+
+    #Background Settings
+    background_fill_primary='*primary_100',
+    background_fill_secondary='*secondary_200',
     
-        partial_message = ""
-        for chunk in Mistralclient.chat.stream(model=model, messages=messages):
-            if chunk.data.choices[0].delta.content is not None:
-                partial_message = partial_message + chunk.data.choices[
-                    0].delta.content
-                yield partial_message
-    except Exception as e:  # 添加通用异常处理
-        print(f"Error: {e}")
-        return "Please upload a photo"
+    color_accent='*primary_300',
 
-
-def query(prompt, negative_prompt, task, steps, sampler, cfg_scale, seed, width, height):
-    result = {"prompt": prompt,"negative_prompt": negative_prompt,"task": task,"steps": steps,"sampler": sampler,"cfg_scale": cfg_scale,"seed": seed, "width": width, "height": height}
-    print(result)
-    try:
-        language = detect(prompt)
-        if language == 'ru':
-            prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
-            print(prompt)
-    except:
-        pass
-
-    prompt = re.sub(r'[^a-zA-Zа-яА-Я\s]', '', prompt)
+    #Border Settings
+    border_color_accent_subdued='*primary_400',
+    border_color_primary='*primary_400',
     
-    cfg = int(cfg_scale)
-    steps = int(steps)
-    seed = int(seed)
-
-    width = 896
-    height = 1152
-
-    if task == "Playground v2":
-        ua = UserAgent()
-        headers = {
-            'user-agent': f'{ua.random}'
-        }
-        client = Client("https://ashrafb-arpr.hf.space/", headers=headers)
-        result = client.predict(prompt, fn_index=0)
-        return result
-            
-    if task == "Artigen v3":
-        ua = UserAgent()
-        headers = {
-            'user-agent': f'{ua.random}'
-        }
-        client = Client("https://ashrafb-arv3s.hf.space/", headers=headers)
-        result = client.predict(prompt,0,"Cinematic", fn_index=0)
-        return result
-    try:
-        with closing(create_connection("wss://google-sdxl.hf.space/queue/join")) as conn:
-            conn.send('{"fn_index":3,"session_hash":""}')
-            conn.send(f'{{"data":["{prompt}, 4k photo","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry",7.5,"(No style)"],"event_data":null,"fn_index":3,"session_hash":""}}')
-            c = 0
-            while c < 60:
-                status = json.loads(conn.recv())['msg']
-                if status == 'estimation':
-                    c += 1
-                    time.sleep(1)
-                    continue
-                if status == 'process_starts':
-                    break
-            photo = json.loads(conn.recv())['output']['data'][0][0]
-            photo = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
-            photo = Image.open(io.BytesIO(base64.decodebytes(bytes(photo, "utf-8"))))
-            return photo
-    except:
-        try:
-            ua = UserAgent()
-            headers = {
-                'authority': 'ehristoforu-dalle-3-xl-lora-v2.hf.space',
-                'accept': 'text/event-stream',
-                'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7',
-                'cache-control': 'no-cache',
-                'referer': 'https://ehristoforu-dalle-3-xl-lora-v2.hf.space/?__theme=light',
-                'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"',
-                'sec-ch-ua-mobile': '?0',
-                'sec-ch-ua-platform': '"Windows"',
-                'sec-fetch-dest': 'empty',
-                'sec-fetch-mode': 'cors',
-                'sec-fetch-site': 'same-origin',
-                'user-agent': f'{ua.random}'
-            }
-            client = Client("ehristoforu/dalle-3-xl-lora-v2", headers=headers)
-            result = client.predict(prompt,"(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",True,0,1024,1024,6,True, api_name='/run')
-            return result[0][0]['image']
-        except:
-            try:
-                ua = UserAgent()
-                headers = {
-                    'authority': 'nymbo-sd-xl.hf.space',
-                    'accept': 'text/event-stream',
-                    'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7',
-                    'cache-control': 'no-cache',
-                    'referer': 'https://nymbo-sd-xl.hf.space/?__theme=light',
-                    'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"',
-                    'sec-ch-ua-mobile': '?0',
-                    'sec-ch-ua-platform': '"Windows"',
-                    'sec-fetch-dest': 'empty',
-                    'sec-fetch-mode': 'cors',
-                    'sec-fetch-site': 'same-origin',
-                    'user-agent': f'{ua.random}'
-                }
-                client = Client("Nymbo/SD-XL", headers=headers)
-                result = client.predict(prompt,negative_prompt,"","",True,False,False,0,896,1152,7,1,25,25,False,api_name="/run")
-                return result
-            except:
-                try:
-                    ua = UserAgent()
-                    headers = {
-                        'authority': 'radames-real-time-text-to-image-sdxl-lightning.hf.space',
-                        'accept': 'text/event-stream',
-                        'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7',
-                        'cache-control': 'no-cache',
-                        'referer': 'https://radames-real-time-text-to-image-sdxl-lightning.hf.space/?__theme=light',
-                        'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"',
-                        'sec-ch-ua-mobile': '?0',
-                        'sec-ch-ua-platform': '"Windows"',
-                        'sec-fetch-dest': 'empty',
-                        'sec-fetch-mode': 'cors',
-                        'sec-fetch-site': 'same-origin',
-                        'user-agent': f'{ua.random}'
-                    }
-                    client = Client("radames/Real-Time-Text-to-Image-SDXL-Lightning", headers=headers)
-                    result = client.predict(prompt, [], 0, random.randint(1, 999999), fn_index=0)
-                    return result
-                except:
-                    try:
-                        ua = UserAgent()
-                        headers = {
-                            'user-agent': f'{ua.random}'
-                        }
-                        client = Client("https://ashrafb-arpr.hf.space/", headers=headers)
-                        result = client.predict(prompt, fn_index=0)
-                        return result
-                    except:
-                        ua = UserAgent()
-                        headers = {
-                            'user-agent': f'{ua.random}'
-                        }
-                        client = Client("https://ashrafb-arv3s.hf.space/", headers=headers)
-                        result = client.predict(prompt,0,"Cinematic", fn_index=0)
-                        return result
-                        
-
-
-def mirror(image_output, scale_by, method, gfpgan, codeformer):
-
-    url_up = "https://darkstorm2150-protogen-web-ui.hf.space/run/predict/"
-    url_up_f = "https://darkstorm2150-protogen-web-ui.hf.space/file="
-
-    scale_by = int(scale_by)
-    gfpgan = int(gfpgan)
-    codeformer = int(codeformer)
-    
-    with open(image_output, "rb") as image_file:
-        encoded_string2 = base64.b64encode(image_file.read())
-        encoded_string2 = str(encoded_string2).replace("b'", '')
-
-    encoded_string2 = "data:image/png;base64," + encoded_string2
-    data = {"fn_index":81,"data":[0,0,encoded_string2,None,"","",True,gfpgan,codeformer,0,scale_by,896,1152,None,method,"None",1,False,[],"",""],"session_hash":""}
-    r = requests.post(url_up, json=data, timeout=100)
-    print(r.text)
-    print(r.json()['data'][0][0]['name'])
-    ph = "https://darkstorm2150-protogen-web-ui.hf.space/file=" + str(r.json()['data'][0][0]['name'])
-    print(ph)
-    response2 = requests.get(ph)
-    img = Image.open(BytesIO(response2.content))
-    return img
-
-css = """
-.gradio-container {background-color: MediumAquaMarine}
-footer{display:none !important}
-#generate {
-    width: 100%;
-}
-#image_output {
-height: 100% !important;
-}
-"""
-
-with gr.Blocks(css=css, theme=theme) as app:
-    with gr.Tab(label="Image To Prompt"):
-            with gr.Row():
-                with gr.Column(scale=4, min_width=300):
-                    input_img = gr.Image(label="Input Picture", type="filepath")
-                with gr.Column(scale=3):
-                    output_text = gr.Textbox(label="Prompt", lines=2, scale=6, show_copy_button = True)
-                    submit_btn = gr.Button(value="Generate Pompt", scale=4, variant='primary')
-                    clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button")
-                    clear_prompt.click(lambda: (None, None), None, [input_img, output_text], queue=False, show_api=False)
-        
-    submit_btn.click(feifeichat, [input_img], [output_text])
-
-    with gr.Tab("Basic Settings"):
-        with gr.Column(scale=3):
-            with gr.Row():
-                prompt = gr.Textbox(placeholder="Enter the image description...", show_label=True, label='Image Prompt ✍️', lines=3, show_copy_button = True)
-            with gr.Row():
-                task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Model of neural network ❄️ ", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL', 
-                                                                                                                  'Juggernaut XL', 'DreamShaper XL',
-                                                                                                                  'SDXL Niji', 'Cinemax SDXL', 'NightVision XL'])
-    with gr.Accordion("Advanced Options", open=True):
-        with gr.Row():
-            negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=True, label='Negative Prompt:', lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry")
-        with gr.Row():    
-            sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method:", choices=[
-                "DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
-        with gr.Row():
-            steps = gr.Slider(show_label=True, label="Sampling Steps:", minimum=1, maximum=50, value=35, step=1)
-        with gr.Row():    
-            cfg_scale = gr.Slider(show_label=True, label="CFG Scale:", minimum=1, maximum=20, value=7, step=1)
-        with gr.Row():    
-            seed = gr.Number(show_label=True, label="Seed:", minimum=-1, maximum=1000000, value=-1, step=1)
-        with gr.Row():    
-            width = gr.Slider(label="Width", minimum=512, maximum=2048, step=8, value=896, interactive=True,)
-        with gr.Row():    
-            height = gr.Slider(label="Height", minimum=512, maximum=2048, step=8, value=1152,interactive=True,)
-        with gr.Column():
-            with gr.Row():
-                image_output = gr.Image(show_download_button=True, interactive=False, label='Generated Image 🌄', show_share_button=False, format="png", type="filepath", elem_id="gallery")
-                text_button = gr.Button("Generate image", variant="primary", interactive=True, elem_id="generate")
-                
-    with gr.Tab("Upscaling Settings"):
-        with gr.Column():
-            with gr.Row():
-                scale_by = gr.Number(show_label=True, label="How many times to increase:", minimum=1, maximum=2, value=2, step=1)
-            with gr.Row():
-                method = gr.Dropdown(show_label=True, value="ESRGAN_4x", label="Increasing algorithm", choices=["ScuNET GAN", "SwinIR 4x", "ESRGAN_4x", "R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"])
-        with gr.Column():
-            with gr.Row():
-                gfpgan = gr.Slider(show_label=True, label="Effect GFPGAN (For facial improvement)", minimum=0, maximum=1, value=0, step=0.1)
-            with gr.Row():
-                codeformer = gr.Slider(show_label=True, label="Effect CodeFormer (Improve the face)", minimum=0, maximum=1, value=0, step=0.1)
+    #Block Settings
+    block_radius='*radius_md',
+    block_background_fill='*primary_200',
+    block_border_color='*primary_500',
+    block_border_width='*panel_border_width',
+    block_info_text_color='*primary_700',
+    block_info_text_size='*text_md',
     
-
-    with gr.Column():
-        #image_output = gr.Image(show_download_button=True, interactive=False, label='Generated Image 🌄', show_share_button=False, format="png", type="filepath", elem_id="gallery")
-        
-        text_button.click(query, inputs=[prompt, negative_prompt, task, steps, sampler, cfg_scale, seed, width, height], outputs=image_output, concurrency_limit=48)
-        clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button")
-        clear_prompt.click(lambda: (None, None), None, [prompt, image_output], queue=False, show_api=False)
-        
-        img2img_b = gr.Button("Increase the image", variant='secondary')
-        image_i2i = gr.Image(show_label=True, label='Increased image:')
-        img2img_b.click(mirror, inputs=[image_output, scale_by, method, gfpgan, codeformer], outputs=image_i2i, concurrency_limit=48)
-        
-
-app.queue(default_concurrency_limit=200, max_size=200)  # <-- Sets up a queue with default parameters
-if __name__ == "__main__":
-    app.launch()
+    container_radius='*radius_xl',
+    panel_background_fill='*primary_200',
+    accordion_text_color='*primary_600',
+    checkbox_border_radius='*radius_xl',
+    #slider_color='*primary_600',
+    table_text_color='*primary_600',
+    input_background_fill='*primary_50',
+    input_background_fill_focus='*primary_100',
+
+    #Button Settings
+    button_border_width='1px',
+    button_transform_hover='scale(1.01)',
+    button_transition='all 0.1s ease-in-out',
+    button_transform_active='Scale(0.9)',
+    button_large_radius='*radius_xl',
+    button_small_radius='*radius_xl',
+    button_primary_border_color='*primary_500',
+    button_secondary_border_color='*primary_400',
+    button_primary_background_fill_hover='linear-gradient(90deg, *primary_400, *secondary_200, *primary_400)',
+    button_primary_background_fill='linear-gradient(90deg,*secondary_300 , *primary_500, *secondary_300)',
+    button_primary_text_color='*primary_100',
+    button_primary_text_color_hover='*primary_700',
+    button_cancel_background_fill='*primary_500',
+    button_cancel_background_fill_hover='*primary_400',
+
+    loader_color="#800000",
+    slider_color="#708090"
+)
+#----------End of theme----------