Update app.py

app.py

@@ -1,195 +1,11 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoModelForCausalLM
+from transformers import AutoProcessor, AutoModelForCausalLM, MarianMTModel, MarianTokenizer
 from PIL import Image
 import torch
 from gtts import gTTS
-import spacy
-import requests
-import nltk.tree
-import re
 import os
 
-# Carregar o modelo de português do spaCy
-nlp = spacy.load("pt_core_news_sm")
-
-# Chave para o LX-Parser
-key = "eb159d39469d84f0ff47167a4d89cada"
-
-# Funções de manipulação gramatical
-def invert_adj_n(doc, tags):
-    frase = []
-    already = False
-    for i in range(len(doc)):
-        if already:
-            already = False
-            continue
-        if doc[i].tag_ != "PUNCT":
-            if tags[i] == "A":
-                if i + 1 < len(tags) and tags[i + 1] == "N":
-                    frase.append(doc[i + 1].text)
-                    frase.append(doc[i].text)
-                    already = True
-                else:
-                    frase.append(doc[i].text)
-            else:
-                frase.append(doc[i].text)
-        else:
-            frase.append(doc[i].text)
-    return frase
-
-def adjust_adj(doc, tags):
-    frase = []
-    for i in range(len(doc)):
-        frase.append(doc[i].text)
-        if tags[i] == "A":
-            if i + 1 < len(tags) and tags[i + 1] == "A":
-                frase.append("e")
-    return frase
-
-def adjust_art(doc, tags):
-    frase = []
-    already = False
-    for i in range(len(doc)):
-        if already:
-            already = False
-            continue
-        text = doc[i].text
-        if tags[i] == "ART" and text.lower() == "a":
-            if i + 1 < len(doc):
-                gender = doc[i + 1].morph.get("Gender")
-                number = doc[i + 1].morph.get("Number")
-                if gender and number:
-                    if gender[0] == "Masc" and number[0] == "Sing":
-                        frase.append("um")
-                    elif gender[0] == "Fem" and number[0] == "Sing":
-                        frase.append("uma")
-                    elif gender[0] == "Masc" and number[0] != "Sing":
-                        frase.append("os")
-                    else:
-                        frase.append("as")
-                else:
-                    frase.append(text)
-            else:
-                frase.append(text)
-        else:
-            frase.append(text)
-    return frase
-
-def create_sentence(doc, tags, frase):
-    tmp = frase
-    for i in range(len(doc)):
-        text = doc[i].text
-        if doc[i].is_sent_start:
-            tmp[i] = tmp[i].capitalize()
-        if doc[i].tag_ == "PUNCT":
-            tmp[i - 1] += text
-    return tmp
-
-def get_productions(texto):
-    format = 'parentheses'
-    url = "https://portulanclarin.net/workbench/lx-parser/api/"
-    request_data = {
-        'method': 'parse',
-        'jsonrpc': '2.0',
-        'id': 0,
-        'params': {
-            'text': texto,
-            'format': format,
-            'key': key,
-        },
-    }
-    request = requests.post(url, json=request_data)
-    response_data = request.json()
-    if "error" in response_data:
-        print("Error:", response_data["error"])
-        return []
-    else:
-        result = response_data["result"]
-        productions = []
-        tree = nltk.tree.Tree.fromstring(result)
-        for tag in tree.productions():
-            if len(re.findall(r"'.*'", str(tag))) > 0:
-                productions.append(str(tag))
-        return productions
-
-def get_tags(productions):
-    tags = []
-    for item in productions:
-        if isinstance(item, str):
-            tags.append(item[:item.find(' ->')])
-        else:
-            tags.append(item)
-    for item in tags:
-        if "'" in item:
-            tags.remove(item)
-    return tags
-
-def reordenar_sentenca(sentenca):
-    if not sentenca.strip():
-        return sentenca
-    sentenca = sentenca.lower()
-    sentence = get_productions(sentenca)
-    tags = get_tags(sentence)
-    doc = nlp(sentenca)
-    if tags[0] != "ART":
-        sentenca = "A " + sentenca.strip()
-    sentence = get_productions(sentenca)
-    tags = get_tags(sentence)
-    doc = nlp(sentenca)
-    if not sentence:
-        return sentenca.strip()
-    aux = []
-    if len(tags) > 2 and tags[1] == "N" and tags[2] == "N":
-        aux = sentenca.split()
-        tmp = aux[1]
-        aux[1] = aux[2]
-        aux.insert(2, "de")
-        aux[3] = tmp
-        sentenca = " ".join(aux)
-        sentence = get_productions(sentenca)
-        tags = get_tags(sentence)
-        doc = nlp(sentenca)
-    frase = []
-    already = False
-    person = 3
-    tmp_doc = []
-    for token in doc:
-        tmp_doc.append(token)
-    frase = invert_adj_n(tmp_doc, tags)
-    nova_sentenca = ' '.join(frase)
-    productions = get_productions(nova_sentenca)
-    tags = get_tags(productions)
-    doc = nlp(nova_sentenca)
-    while nova_sentenca != sentenca:
-        frase = invert_adj_n(doc, tags)
-        sentenca = nova_sentenca
-        nova_sentenca = ' '.join(frase)
-        productions = get_productions(nova_sentenca)
-        tags = get_tags(productions)
-        doc = nlp(nova_sentenca)
-    frase = adjust_adj(doc, tags)
-    nova_sentenca = ' '.join(frase)
-    productions = get_productions(nova_sentenca)
-    tags = get_tags(productions)
-    doc = nlp(nova_sentenca)
-    while nova_sentenca != sentenca:
-        frase = adjust_adj(doc, tags)
-        sentenca = nova_sentenca
-        nova_sentenca = ' '.join(frase)
-        productions = get_productions(nova_sentenca)
-        tags = get_tags(productions)
-        doc = nlp(nova_sentenca)
-    frase = adjust_art(doc, tags)
-    sentenca = ' '.join(frase)
-    productions = get_productions(sentenca)
-    tags = get_tags(productions)
-    doc = nlp(sentenca)
-    frase = create_sentence(doc, tags, frase)
-    sentenca_normalizada = ""
-    for i in range(len(frase)):
-        sentenca_normalizada += frase[i] + " "
-    return sentenca_normalizada.strip()
-
+# Funções auxiliares
 def prepare_image(image_path):
     image = Image.open(image_path).convert("RGB")
     inputs = processor(images=image, return_tensors="pt").to(device)
@@ -207,40 +23,48 @@ def generate_caption(pixel_values):
         )
     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("histlearn/microsoft-git-portuguese-neuro-simbolic")
-model = AutoModelForCausalLM.from_pretrained("histlearn/microsoft-git-portuguese-neuro-simbolic")
+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_pt = generate_caption(pixel_values)
-    sentenca_normalizada = reordenar_sentenca(caption_pt)
-    audio_file = text_to_speech_gtts(sentenca_normalizada)
-    productions = get_productions(sentenca_normalizada)
-    return sentenca_normalizada, productions, audio_file
+    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
 
-# Caminhos para as imagens de exemplo
+# Caminhos para as imagens de exemplo (supondo que estejam no mesmo diretório que o script)
 example_image_paths = [
-    "example1.jpeg",
-    "example2.jpeg",
-    "example3.jpeg"
+    "example1.png",
+    "example2.png",
+    "example3.png"
 ]
 
 # Interface Gradio
 iface = gr.Interface(
     fn=process_image,
     inputs=gr.Image(type="filepath"),
-    outputs=[gr.Textbox(label="Sentença Normalizada"), gr.Textbox(label="Classes Gramaticais"), gr.Audio(type="filepath", label="Áudio")],
+    outputs=[gr.Textbox(), gr.Audio(type="filepath")],
     examples=example_image_paths,
     title="Image to Voice",
     description="Gera uma descrição em português e a converte em voz a partir de uma imagem."