Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import requests
 from PIL import Image
 from transformers import BlipProcessor, BlipForConditionalGeneration, pipeline
 import time
@@ -11,32 +10,42 @@ model_blip = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image
 # Carregando um modelo de geração de texto (exemplo: GPT-2)
 generator = pipeline('text-generation', model='gpt2')
 
+# Função para gerar legenda da imagem
 def caption(img, min_len, max_len):
     raw_image = Image.open(img).convert('RGB')
     inputs = processor(raw_image, return_tensors="pt")
     out = model_blip.generate(**inputs, min_length=min_len, max_length=max_len)
     return processor.decode(out[0], skip_special_tokens=True)
 
-def generate_nutritional_info(food_description):
-    # Gerando informações nutricionais com base na descrição do alimento
-    prompt = f"Provide detailed nutritional information about {food_description}."
+# Função para gerar informações nutricionais e calorias
+def generate_nutritional_info(food_description, language):
+    if language == "Português":
+        prompt = f"Descreva as informações nutricionais e as calorias do seguinte alimento: {food_description}."
+    else:
+        prompt = f"Provide detailed nutritional information and calories for the following food: {food_description}."
+    
     result = generator(prompt, max_length=150, num_return_sequences=1)
     return result[0]['generated_text']
 
-def greet(img, min_len, max_len):
+# Função principal que combina tudo
+def greet(img, min_len, max_len, language):
     start = time.time()
     
     # Passo 1: Gerar legenda para a imagem
     food_description = caption(img, min_len, max_len)
     
-    # Passo 2: Gerar informações nutricionais com base na legenda
-    nutritional_info = generate_nutritional_info(food_description)
+    # Passo 2: Gerar informações nutricionais e calorias com base na legenda
+    nutritional_info = generate_nutritional_info(food_description, language)
     
     end = time.time()
     total_time = str(end - start)
     
     # Combinando resultados
-    result = f"Food Description: {food_description}\n\nNutritional Information:\n{nutritional_info}\n\nGenerated in {total_time} seconds."
+    if language == "Português":
+        result = f"Descrição do Alimento: {food_description}\n\nInformações Nutricionais:\n{nutritional_info}\n\nGerado em {total_time} segundos."
+    else:
+        result = f"Food Description: {food_description}\n\nNutritional Information:\n{nutritional_info}\n\nGenerated in {total_time} seconds."
+    
     return result
 
 # Interface Gradio
@@ -47,7 +56,8 @@ iface = gr.Interface(
     inputs=[
         gr.Image(type='filepath', label='Image'),
         gr.Slider(label='Minimum Length', minimum=1, maximum=1000, value=30),
-        gr.Slider(label='Maximum Length', minimum=1, maximum=1000, value=100)
+        gr.Slider(label='Maximum Length', minimum=1, maximum=1000, value=100),
+        gr.Radio(choices=["Português", "English"], label="Language", value="Português")  # Botão de seleção de idioma
     ],
     outputs=gr.Textbox(label='Result'),
     theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", neutral_hue="slate"),