Create your Gradio app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+from clip_interrogator import Config, Interrogator
+import random
+import re
+import requests
+import shutil
+from PIL import Image
+
+
+# Definir la función para generar prompt desde imagen
+config = Config()
+config.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+config.blip_offload = False if torch.cuda.is_available() else True
+config.chunk_size = 2048
+config.flavor_intermediate_count = 512
+config.blip_num_beams = 64
+config.clip_model_name = "ViT-H-14/laion2b_s32b_b79k"
+ci = Interrogator(config)
+
+def get_prompt_from_image(image, mode):
+    image = image.convert('RGB')
+    if mode == 'best':
+        prompt = ci.interrogate(image)
+    elif mode == 'classic':
+        prompt = ci.interrogate_classic(image)
+    elif mode == 'fast':
+        prompt = ci.interrogate_fast(image)
+    elif mode == 'negative':
+        prompt = ci.interrogate_negative(image)
+    return prompt
+
+
+# Definir la función para generar prompt desde texto
+model = AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-zh-en').eval()
+tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-zh-en')
+
+def translate(text):
+    with torch.no_grad():
+        encoded = tokenizer([text], return_tensors='pt')
+        sequences = model.generate(**encoded)
+        return tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+
+text_pipe = pipeline('text-generation', model='succinctly/text2image-prompt-generator')
+
+def text_generate(input):
+    seed = random.randint(100, 1000000)
+    set_seed(seed)
+    text_in_english = translate(input)
+    for count in range(6):    
+        sequences = text_pipe(text_in_english, max_length=random.randint(60, 90), num_return_sequences=8)
+        list = []
+        for sequence in sequences:
+            line = sequence['generated_text'].strip()
+            if line != text_in_english and len(line) > (len(text_in_english) + 4) and line.endswith((':', '-', '—')) is False:
+                list.append(line)
+
+        result = "\n".join(list)
+        result = re.sub('[^ ]+\.[^ ]+','', result)
+        result = result.replace('<', '').replace('>', '')
+        if result != '':
+            return result
+        if count == 5:
+            return result
+
+
+# Definir la función que permite al usuario cargar una imagen desde una URL
+def load_image_from_url(url):
+    response = requests.get(url, stream=True)
+    if response.status_code == 200:
+        with open('./image.jpg', 'wb') as f:
+            response.raw.decode_content = True
+            shutil.copyfileobj(response.raw, f) 
+        return Image.open('./image.jpg')
+    else:
+        raise ValueError("No se pudo cargar la imagen")
+
+
+# Crear la interfaz de usuario de Gradio
+with gr.Interface(
+    [get_prompt_from_image, text_generate],
+    [
+        gr.inputs.Image(type='pil', label='Imagen'),
+        gr.inputs.Radio(['best', 'fast', 'classic', 'negative'], value='best', label='Modo'),
+        gr.inputs.Textbox(lines=6, label='Texto de entrada'),
+    ],
+    [