Update main.py

main.py

@@ -5,77 +5,6 @@ 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():
@@ -94,72 +23,5 @@ def faceswapper():
 
     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)