Update app.py

app.py

@@ -1,12 +1,10 @@
 import gradio as gr
 import requests
 import os
-import random
 import base64
 from PIL import Image
 import io
 
-
 def resize_image(image_path, max_size=(800, 800), quality=85):
     with Image.open(image_path) as img:
         img.thumbnail(max_size, Image.Resampling.LANCZOS)
@@ -19,28 +17,14 @@ def filepath_to_base64(image_path):
     img_base64 = base64.b64encode(img_bytes)
     return f"data:image/jpeg;base64,{img_base64.decode('utf-8')}"
 
-# A chave da API é lida de uma variável de ambiente para maior segurança.
 api_key = os.getenv('API_KEY')
 
-def call_fuyu_8b_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens=1024, seed=None):
+def call_fuyu_8b_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens=1024):
     print(f"Caminho da imagem recebida: {image_path}")
     print(f"Conteúdo: {content}")
     print(f"Temperatura: {temperature}")
     print(f"Top P: {top_p}")
     print(f"Max Tokens: {max_tokens}")
-    
-    # Convertendo e validando o seed
-    if seed is not None:
-        try:
-            seed = int(seed)
-            print(f"Seed utilizado: {seed}")
-        except ValueError:
-            # Se o seed fornecido não puder ser convertido para inteiro, retorna erro
-            return "Seed must be an integer."
-    else:
-        # Gera um seed aleatório se nenhum for fornecido
-        seed = random.randint(0, 18446744073709551615)
-        print(f"Seed gerado aleatoriamente: {seed}")
 
     image_base64 = filepath_to_base64(image_path)
     invoke_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/9f757064-657f-4c85-abd7-37a7a9b6ee11"
@@ -60,8 +44,7 @@ def call_fuyu_8b_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens
         "temperature": temperature,
         "top_p": top_p,
         "max_tokens": max_tokens,
-        "stream": True,
-        "seed": seed
+        "stream": True
     }
 
     response = requests.post(invoke_url, headers=headers, json=payload, stream=True)
@@ -81,20 +64,17 @@ def call_fuyu_8b_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens
                     response_text += decoded_line.split('"content":"')[1].split('","finish_reason')[0]
         
         return response_text
-# Definindo os componentes da interface
+
 content_input = gr.Textbox(lines=2, placeholder="Enter your content here...", label="Content")
 image_input = gr.Image(type="filepath", label="Upload Image")
 temperature_input = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.2, label="Temperature")
 top_p_input = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label="Top P")
 max_tokens_input = gr.Slider(minimum=1, maximum=1024, step=1, value=1024, label="Max Tokens")
-seed_input = gr.Textbox(label="Seed (optional)")
 
-# Criando a interface Gradio
 iface = gr.Interface(fn=call_fuyu_8b_api,
-                     inputs=[image_input, content_input, temperature_input, top_p_input, max_tokens_input, seed_input],
+                     inputs=[image_input, content_input, temperature_input, top_p_input, max_tokens_input],
                      outputs="text",
                      title="Fuyu-8B API Explorer",
                      description="Explore the capabilities of Fuyu-8B multi-modal transformer.")
 
-# Executando a interface
-iface.launch()
+iface.launch()