Update app.py

app.py

@@ -1,67 +1,66 @@
-import gradio as gr
-from transformers import AutoProcessor, AutoModelForCausalLM, MarianMTModel, MarianTokenizer
-from PIL import Image
-import torch
-import matplotlib.pyplot as plt
-from gtts import gTTS
-from IPython.display import Audio
-
-# Funções auxiliares
-def prepare_image(image_path):
-    image = Image.open(image_path).convert("RGB")
-    inputs = processor(images=image, return_tensors="pt").to(device)
-    return image, inputs.pixel_values
-
-def generate_caption(pixel_values):
-    model.eval()
-    with torch.no_grad():
-        generated_ids = model.generate(
-            pixel_values=pixel_values,
-            max_length=50,
-            num_beams=4,
-            early_stopping=True,
-            no_repeat_ngram_size=2
-        )
-    return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-def translate_to_portuguese(text):
-    inputs = translation_tokenizer(text, return_tensors="pt", truncation=True).to(device)
-    translated_ids = translation_model.generate(inputs["input_ids"], max_length=50, num_beams=4, early_stopping=True)
-    return translation_tokenizer.batch_decode(translated_ids, skip_special_tokens=True)[0]
-
-def text_to_speech_gtts(text, lang='pt'):
-    tts = gTTS(text=text, lang=lang)
-    tts.save("output.mp3")
-    return "output.mp3"
-
-# Carregar os modelos
-processor = AutoProcessor.from_pretrained("microsoft/git-large-textcaps")
-model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-textcaps")
-translation_model_name = 'Helsinki-NLP/opus-mt-tc-big-en-pt'
-translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)
-translation_model = MarianMTModel.from_pretrained(translation_model_name)
-
-# Configurar o dispositivo (GPU ou CPU)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
-translation_model.to(device)
-
-# Função principal para processar a imagem e gerar a voz
-def image_to_voice(image):
-    image, pixel_values = prepare_image(image)
-    caption_en = generate_caption(pixel_values)
-    caption_pt = translate_to_portuguese(caption_en)
-    audio_file = text_to_speech_gtts(caption_pt)
-    return caption_pt, audio_file
-
-# Interface Gradio
-demo = gr.Interface(
-    fn=image_to_voice,
-    inputs=gr.inputs.Image(type="filepath"),
-    outputs=[gr.outputs.Textbox(), gr.outputs.Audio(type="file")],
-    title="Image to Voice",
-    description="Gera uma descrição em português e a converte em voz a partir de uma imagem."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM, MarianMTModel, MarianTokenizer
+from PIL import Image
+import torch
+from gtts import gTTS
+import os
+
+# Funções auxiliares
+def prepare_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    return image, inputs.pixel_values
+
+def generate_caption(pixel_values):
+    model.eval()
+    with torch.no_grad():
+        generated_ids = model.generate(
+            pixel_values=pixel_values,
+            max_length=50,
+            num_beams=4,
+            early_stopping=True,
+            no_repeat_ngram_size=2
+        )
+    return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+def translate_to_portuguese(text):
+    inputs = translation_tokenizer(text, return_tensors="pt", truncation=True).to(device)
+    translated_ids = translation_model.generate(inputs["input_ids"], max_length=50, num_beams=4, early_stopping=True)
+    return translation_tokenizer.batch_decode(translated_ids, skip_special_tokens=True)[0]
+
+def text_to_speech_gtts(text, lang='pt'):
+    tts = gTTS(text=text, lang=lang)
+    tts.save("output.mp3")
+    return "output.mp3"
+
+# Carregar os modelos
+processor = AutoProcessor.from_pretrained("microsoft/git-base")
+model = AutoModelForCausalLM.from_pretrained("microsoft/git-base")
+translation_model_name = 'Helsinki-NLP/opus-mt-tc-big-en-pt'
+translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)
+translation_model = MarianMTModel.from_pretrained(translation_model_name)
+
+# Configurar o dispositivo (GPU ou CPU)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+translation_model.to(device)
+
+# Função principal para processar a imagem e gerar a voz
+def process_image(image):
+    _, pixel_values = prepare_image(image)
+    caption_en = generate_caption(pixel_values)
+    caption_pt = translate_to_portuguese(caption_en)
+    audio_file = text_to_speech_gtts(caption_pt)
+    return caption_pt, audio_file
+
+# Interface Gradio
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="filepath"),
+    outputs=[gr.Textbox(), gr.Audio(type="filepath")],
+    title="Image to Voice",
+    description="Gera uma descrição em português e a converte em voz a partir de uma imagem."
+)
+
+if __name__ == "__main__":
+    iface.launch()