Create main.py

main.py

@@ -0,0 +1,165 @@
+import requests
+from io import BytesIO
+from flask import Flask, request, jsonify
+from gradio_client import Client
+from huggingface_hub import create_repo, upload_file
+
+app = Flask(__name__)
+
+@app.route('/run', methods=['POST'])
+def run_model():
+      # Obter parâmetros da consulta da URL
+      endpoint = request.args.get('endpoint', default='https://pierroromeu-zbilatuca2testzz.hf.space')
+      prompt = request.args.get('prompt', default='Hello!!')
+      negative_prompt = request.args.get('negative_prompt', default='Hello!!')
+      prompt_2 = request.args.get('prompt_2', default='Hello!!')
+      negative_prompt_2 = request.args.get('negative_prompt_2', default='Hello!!')
+      use_negative_prompt = request.args.get('use_negative_prompt', type=bool, default=True)
+      use_prompt_2 = request.args.get('use_prompt_2', type=bool, default=True)
+      use_negative_prompt_2 = request.args.get('use_negative_prompt_2', type=bool, default=False)
+      seed = request.args.get('seed', type=int, default=0)
+      width = request.args.get('width', type=int, default=256)
+      height = request.args.get('height', type=int, default=256)
+      guidance_scale = request.args.get('guidance_scale', type=float, default=5.5)
+      num_inference_steps = request.args.get('num_inference_steps', type=int, default=50)
+      strength = request.args.get('strength', type=float, default=0.7)
+      use_vae_str = request.args.get('use_vae', default='false')  # Obtém use_vae como string
+      use_vae = use_vae_str.lower() == 'true'  # Converte para booleano
+      use_lora_str = request.args.get('use_lora', default='false')  # Obtém use_lora como string
+      use_lora = use_lora_str.lower() == 'true'  # Converte para booleano
+      use_img2img_str = request.args.get('use_img2img', default='false')  # Obtém use_vae como string
+      use_img2img = use_img2img_str.lower() == 'true'  # Converte para booleano
+      model = request.args.get('model', default='stabilityai/stable-diffusion-xl-base-1.0')
+      vaecall = request.args.get('vaecall', default='madebyollin/sdxl-vae-fp16-fix')
+      lora = request.args.get('lora', default='amazonaws-la/sdxl')
+      lora_scale = request.args.get('lora_scale', type=float, default=0.7)
+      url = request.args.get('url', default='https://example.com/image.png')
+
+      # Chamar a API Gradio
+      client = Client(endpoint)
+      result = client.predict(
+          prompt, negative_prompt, prompt_2, negative_prompt_2,
+          use_negative_prompt, use_prompt_2, use_negative_prompt_2,
+          seed, width, height,
+          guidance_scale,
+          num_inference_steps,
+          strength,
+          use_vae,
+          use_lora,
+          model,
+          vaecall,
+          lora,
+          lora_scale,
+          use_img2img,
+          url,
+          api_name="/run"
+      )
+
+      return jsonify(result)
+
+@app.route('/predict', methods=['POST'])
+def predict_gan():
+    # Obter parâmetros da consulta da URL
+    endpoint = request.args.get('endpoint', default='https://pierroromeu-gfpgan.hf.space/--replicas/dgwcd/')
+    hf_token = request.args.get('hf_token', default='')
+    filepath = request.args.get('filepath', default='')
+    version = request.args.get('version', default='v1.4')
+    rescaling_factor = request.args.get('rescaling_factor', type=float, default=2.0)
+
+    # Chamar a API Gradio
+    client = Client(endpoint, hf_token=hf_token)
+    result = client.predict(
+        filepath,
+        version,
+        rescaling_factor,
+        api_name="/predict"
+    )
+
+    return jsonify(result)
+
+@app.route('/faceswapper', methods=['POST'])
+def faceswapper():
+    # Obter parâmetros da consulta da URL
+    endpoint = request.args.get('endpoint', default='https://pierroromeu-faceswapper.hf.space/--replicas/u42x7/')
+    user_photo = request.args.get('user_photo', default='')
+    result_photo = request.args.get('result_photo', default='')
+  
+    # Chamar a API Gradio
+    client = Client(endpoint)
+    result = client.predict(
+        user_photo,
+        result_photo,
+        api_name="/predict"
+    )
+
+    return jsonify(result)
+
+@app.route('/train', methods=['POST'])
+def answer():
+    # Obter parâmetros da consulta da URL
+    token = request.args.get('token', default='')
+    endpoint = request.args.get('endpoint', default='https://pierroromeu-gfpgan.hf.space/--replicas/dgwcd/')
+    dataset_id=request.args.get('dataset_id', default='')
+    output_model_folder_name=request.args.get('output_model_folder_name', default='')
+    concept_prompt=request.args.get('concept_prompt', default='')
+    max_training_steps=request.args.get('max_training_steps', type=int, default=0)
+    checkpoints_steps=request.args.get('checkpoints_steps', type=int, default=0)
+    remove_gpu_after_training_str = request.args.get('remove_gpu_after_training', default='false')  # Obtém como string
+    remove_gpu_after_training = remove_gpu_after_training_str.lower() == 'true'  # Converte para booleano
+
+    # Chamar a API Gradio
+    client = Client(endpoint, hf_token=token)
+    result = client.predict(
+        dataset_id,
+        output_model_folder_name,
+        concept_prompt,
+        max_training_steps,
+        checkpoints_steps,
+        remove_gpu_after_training,
+        api_name="/main"
+    )
+
+    return jsonify(result)
+
+@app.route('/verify', methods=['GET'])
+# ‘/’ URL is bound with hello_world() function.
+def hello_world():
+    return jsonify('Check')
+
+@app.route('/upload_model', methods=['POST'])
+def upload_model():
+    # Parâmetros
+    file_name= request.args.get('file_name', default='')
+    repo = request.args.get('repo', default='')
+    url = request.args.get('url', default='')
+    token = request.args.get('token', default='')
+
+    try:
+        # Crie o repositório
+        repo_id = repo
+        create_repo(repo_id=repo_id, token=token)
+
+        # Faça o download do conteúdo da URL em memória
+        response = requests.get(url)
+        if response.status_code == 200:
+            # Obtenha o conteúdo do arquivo em bytes
+            file_content = response.content
+            # Crie um objeto de arquivo em memória
+            file_obj = BytesIO(file_content)
+            # Faça o upload do arquivo
+            upload_file(
+                path_or_fileobj=file_obj,
+                path_in_repo=file_name,
+                repo_id=repo_id,
+                token=token
+            )
+
+            # Mensagem de sucesso
+            return jsonify({"message": "Sucess"})
+        else:
+            return jsonify({"error": "Failed"}), 500
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)