{"cells":[{"cell_type":"markdown","id":"48470cbd","metadata":{"id":"48470cbd"},"source":["\n","# Projeto Final – Machine Learning e Deep Learning (PLN: Análise de Sentimentos)\n","\n","**Professor Rodrigo aqui!** \n","Este notebook é o guia didático para o **Projeto Final**. Vamos construir uma solução completa de **Classificação de Sentimentos** usando avaliações da Amazon (**dataset `amazon_polarity` do Hugging Face**), cobrindo todo o pipeline:\n","\n","1. Definição do problema e escolha do dataset \n","2. Coleta/limpeza, preparação e divisão do conjunto de dados \n","3. **Baseline** com *Machine Learning tradicional* (TF-IDF + Regressão Logística) \n","4. Modelo de *Deep Learning* com **LSTM (PyTorch)** \n","5. Avaliação com métricas adequadas (Accuracy, F1, Matriz de Confusão) \n","6. Exportação dos artefatos e **deploy** com **Gradio** (+ passo a passo para publicar no **Hugging Face Spaces**) \n","\n","> **Importante**: Execute célula por célula e leia as explicações. Onde houver blocos \"Experimente\", preencha as suas observações. Esse notebook pode ser entregue como parte dos **entregáveis** do projeto.\n","\n","---\n","\n","## Objetivo Geral\n","Desenvolver uma solução prática de **ML + DL** aplicada a um problema de **PLN** (classificação binária de sentimento), integrando desde a preparação até o deploy em ambiente público gratuito.\n","\n","## Entregáveis\n","- Notebook **.ipynb** com comentários e resultados \n","- **README.md** do projeto (modelo fornecido) \n","- Deploy funcional com **Gradio** (arquivos `app.py` e `requirements.txt` prontos) \n","- Relatório (5–8 páginas) — usar o modelo do README como base\n","\n","---\n","\n","> **Dica para execução no Google Colab**: ative GPU (Menu: Runtime → Change runtime type → **GPU**).\n"]},{"cell_type":"code","execution_count":1,"id":"f8e7be1b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f8e7be1b","executionInfo":{"status":"ok","timestamp":1762969993412,"user_tz":180,"elapsed":9,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"4eb46da3-fe2b-42ea-e64b-8f8baf9a54b8"},"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Ambiente pronto (ajuste as instalações se necessário).\n"]}],"source":["\n","# @title Instalação de dependências (Colab)\n","# Se estiver no Colab, descomente as linhas abaixo para instalar.\n","# Em ambiente local com venv, rode `pip install -r requirements.txt`.\n","\n","# !pip install -q datasets==3.0.1 scikit-learn==1.5.2 matplotlib==3.9.2 torch==2.4.1 \\\n","# pandas==2.2.2 numpy==2.1.3 gradio==5.7.1 tqdm==4.66.5\n","\n","print(\"✅ Ambiente pronto (ajuste as instalações se necessário).\")\n"]},{"cell_type":"code","execution_count":2,"id":"99d5bff0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"99d5bff0","executionInfo":{"status":"ok","timestamp":1762970009339,"user_tz":180,"elapsed":14119,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"d92a2d96-5ff8-4353-9bc8-5fb77793976b"},"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Imports OK\n"]}],"source":["\n","# @title Importações centrais\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tqdm import tqdm\n","from datasets import load_dataset\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.pipeline import Pipeline\n","from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report\n","import joblib\n","import os\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","\n","SEED = 42\n","np.random.seed(SEED)\n","torch.manual_seed(SEED)\n","print(\"✅ Imports OK\")\n"]},{"cell_type":"markdown","id":"dde7d907","metadata":{"id":"dde7d907"},"source":["\n","## 1) Definição do Problema\n","\n","**Tarefa**: Classificar avaliações de produtos como **positivas (1)** ou **negativas (-1)**. \n","**Dataset**: `amazon_polarity` (Hugging Face Datasets). \n","**Justificativa**: análise de sentimentos é amplamente usada em e-commerce e suporte a decisões.\n","\n","> **Critérios de avaliação**: accuracy, F1, matriz de confusão; comparação entre baseline (ML) e LSTM (DL).\n"]},{"cell_type":"code","execution_count":3,"id":"4b875e79","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":603,"referenced_widgets":["28a5100c868e4bc3b6d431aa7688ba7a","00476f0ede5f42518937ded9861f890d","ddeeff0fcbb4456c979f2201f320214c","543ad9465d6648ab97be1dbe41f156e6","55461d8ba84348c3901790a2dadcdea9","3856d00179e94af4a2d7dd424b3d113d","f30de77d68c74cf3b184bb3aaaea17c9","f2b6656db10c4338b4f818bb55e5659e","e012b659e26641aa85c951cc4876cbf2","f4e0943491f44bd485b79b0bcd72efc9","d8e6f0892f534701aac8562a8facce65","9bd673d2112243dcb341fed4aae6c3f8","20bf68fc635d432491410f013d62f8c4","765197e5c33e4115b10971a4bdd7dff6","7ba46ef5b73b4935bf0414bbefbb1ea8","d867c891e22742b190385a5f5b6cab06","491902b8c0c2489e8c6fa98ce9d0022b","9ab2aef3590e42f68fdc42fd7026e52e","6583aa1322e64d18aa7524edc924d6fa","a8d1abe3674e400d8564b1de42638167","dca974250abb407598132820b77585c9","2ba1e1dc1e4a4a9ab198212aee0378a3","6caccdc5d75e4a81871295aecfb64cf9","ab18a8aceb0f48438e5d8385dca3d764","b39831b3d74b4eb383c463f62b9ce7e2","a35cdc3d467a4a5c8d0d2c354bee0c6f","9fb9ed28d4f544b3aee454a16d9b5495","668751256fc74ac180247a5b6102acc6","b92ae0dbfa27484eae49fadbc98f0623","1f2ee0e09bf54798ab342989fa6b7f92","9730559d33f242398d26049721b12e38","12af04bdf93b4521b33ed1d6a0fba277","5b1b65f91fe047ff94fc3aa3ab36bd5e","97ffdaf1a2884498b299f4bae2525871","67c2989964814cba9c8f6244a3628238","c2523028bfa2418981949e9c98a03e8a","e8aa0273403642348b5f4683f2127b66","cd9edefbeab243c99b59aa73f2f2ffa3","d8f7ae158dfc4930b51b36e01d79b8f7","444987e578c74b01bf676339aa5a4d26","e9f158d656b84a298989af09ace6dab6","3aa401d9a36341a9abafcaf65a8b2c13","d343029e050049559a00af725f490210","9af10f88deed4579af2117027674b119","e9a01b058e2749188c99bbfd23fb736e","7c3dc9d863c0443f8bf57412843bbf8a","c316f1edf28547b39b63ff2b14824595","785fce8ae5bc4d0083e07b7e33f961be","846c851e844b43958115ce50e6742b27","4776d0f51cf940e7a7bd8dedd7d6827c","542be3d908d74e13a80b882489d3368c","29fc8dbc59a24987b938b1e743f1468d","528fa4fa13df48528065106df45a1aa9","3134b0148fa14515944adf614c48967e","910d8f2558fb4131945b7c9c9dc54ddd","d631b91838304564b15e8c55f4635118","906f3cf697eb4c90933abf098a3a9d6f","00f23a6ac7ef4142bb02aa2debcf418c","86069c48f2b142bfa7eaf6536db55fa0","b40739dd55a24772965c513dff8edcbb","98ce5ff4c9414b0eb7f84e4c3ee2c07e","44426a8f63224f2da6e648daafc3b212","ba79ed97383045c4bacec2949f746d97","d2da244ca94741afb2e5a667f72bf7d2","381698061555465c810119026d30096e","3845387a395a450493bad84d1da44887","c2f417686f7449baa20384f1fc23d48d","fd4d17f20ef6411f9bf039fef31e1b74","2734cb9e8f3147dbb34a71ec81dd2003","57fbb053fbae4befa715a9fa25a43969","b8b2ce55c87247fcb07a0916b08f9f7e","6377cb99d22644ac98f872739d06b06e","586bbe331f654b3a9db794f8619d0913","239f61adfdfc4cac9f724e0941da864f","ce94451266a345a080cbfc0f6d596c0d","25984323a64d42c1a47ff6d0fa11e843","aa006c2fd45d40a7a0ee465d2763bfaa","e36090c489754453b720f76d267dfa7d","db24acaf110a4d6ab4e1f44d6ea6a2f4","2000cb587d754f5e98e48425e5e4f672","96a1e57064a14eaeabc5907eeaaac6ad","dc8a27479cc94b64af0dece399644c1f","ae54fa359bcf423a96be36acecb5014b","0d54c1f30a4c4159a791c9b2ed56bec9","2ae7955968494ea2ba72c3a2d6090cf9","d8ec7771d4314de194e2217c5a93fe9c","a6c3a66c4b304673a5744bca05fcd100","20e9697419c04643886a325cea3821a9"]},"id":"4b875e79","executionInfo":{"status":"ok","timestamp":1762970172126,"user_tz":180,"elapsed":159871,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"a4aa81a7-efc5-4aa8-f4d9-2d821e079f99"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"28a5100c868e4bc3b6d431aa7688ba7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["amazon_polarity/train-00000-of-00004.par(…): 0%| | 0.00/260M [00:00\n","
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\n"," \n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_train","summary":"{\n \"name\": \"df_train\",\n \"rows\": 12000,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12000,\n \"samples\": [\n \"If your home library shelves have good old Missionary stories, than this is a MUST READ and MUST HAVE!\",\n \"The story of Mary Pinchot Meyer is a lot more interesting than this book. Occasionally, the author tries to recreate scenes and conversations on a pretty slim set of facts, supposing what may have motivated very private people she never met.Oh, and Dean Acheson was not *Under-Secretary* of State! Did this woman read anything about diplomacy, the Cold War, or Washington society between 1940 and 1965? How could she and her copy editor not know that Dean Acheson was our Secretary of State, and a major figure in post-war Washington?Washington was a very exciting place to be -- but you won't get the full description of those times in this book. too bad.\",\n \"Deb gets a taste at temptation from a man who knows what he wants. This book had me hooked. I couldn't put it down until I had to force my self to sleep. This is a great book to read.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":3}],"source":["\n","# @title Coleta e preparação dos dados (amostragem para execução rápida)\n","# Carrega partições 'train' e 'test' do dataset amazon_polarity\n","ds_train = load_dataset(\"amazon_polarity\", split=\"train\")\n","ds_test = load_dataset(\"amazon_polarity\", split=\"test\")\n","\n","# Convertendo para DataFrame\n","df_train = pd.DataFrame({\"text\": ds_train[\"content\"], \"label\": ds_train[\"label\"]})\n","df_test = pd.DataFrame({\"text\": ds_test[\"content\"], \"label\": ds_test[\"label\"]})\n","\n","# O dataset possui rótulos {0,1}; vamos mapeá-los para {-1, +1} opcionalmente para leitura humana\n","label_map = {0:0, 1:1} # manter 0/1 para facilitar as métricas de sklearn\n","df_train[\"label\"] = df_train[\"label\"].map(label_map)\n","df_test[\"label\"] = df_test[\"label\"].map(label_map)\n","\n","# Amostragem para acelerar (ajuste conforme sua GPU/tempo):\n","N_TRAIN = 12000 # experimente 50k+ com GPU boa\n","N_TEST = 6000\n","df_train = df_train.sample(n=N_TRAIN, random_state=SEED).reset_index(drop=True)\n","df_test = df_test.sample(n=N_TEST, random_state=SEED).reset_index(drop=True)\n","\n","# Split treino/val\n","train_text, val_text, train_y, val_y = train_test_split(\n"," df_train[\"text\"].values, df_train[\"label\"].values, test_size=0.2, random_state=SEED, stratify=df_train[\"label\"].values\n",")\n","\n","print(\"Tamanhos: \", len(train_text), len(val_text), len(df_test))\n","df_train.head()\n"]},{"cell_type":"markdown","id":"ed2e0c79","metadata":{"id":"ed2e0c79"},"source":["\n","## 2) Baseline com Machine Learning Tradicional\n","\n","Vamos iniciar com um pipeline simples: **TF-IDF** para vetorização + **Regressão Logística**. \n","Depois, comparamos com um **Random Forest** para observar diferenças.\n"]},{"cell_type":"code","execution_count":4,"id":"d5d7ba98","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":767},"id":"d5d7ba98","executionInfo":{"status":"ok","timestamp":1762970177123,"user_tz":180,"elapsed":4992,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"5679da6c-2a82-49d9-a449-868739cf5554"},"outputs":[{"output_type":"stream","name":"stdout","text":["Val Accuracy: 0.8458333333333333\n","Val F1: 0.8458344040095377\n","\n","Test Accuracy: 0.8528333333333333\n","Test F1: 0.8528318248045601\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Relatório de Classificação (Teste):\n","\n"," precision recall f1-score support\n","\n"," 0 0.85 0.85 0.85 2984\n"," 1 0.85 0.86 0.85 3016\n","\n"," accuracy 0.85 6000\n"," macro avg 0.85 0.85 0.85 6000\n","weighted avg 0.85 0.85 0.85 6000\n","\n"]}],"source":["\n","# @title Treino e avaliação: TF-IDF + Regressão Logística\n","baseline_pipe = Pipeline([\n"," (\"tfidf\", TfidfVectorizer(max_features=40000, ngram_range=(1,2))),\n"," (\"clf\", LogisticRegression(max_iter=1000, n_jobs=None))\n","])\n","\n","baseline_pipe.fit(train_text, train_y)\n","\n","val_pred = baseline_pipe.predict(val_text)\n","test_pred = baseline_pipe.predict(df_test[\"text\"].values)\n","\n","print(\"Val Accuracy:\", accuracy_score(val_y, val_pred))\n","print(\"Val F1:\", f1_score(val_y, val_pred, average=\"weighted\"))\n","print(\"\\nTest Accuracy:\", accuracy_score(df_test[\"label\"].values, test_pred))\n","print(\"Test F1:\", f1_score(df_test[\"label\"].values, test_pred, average=\"weighted\"))\n","\n","# Matriz de confusão (teste)\n","cm = confusion_matrix(df_test[\"label\"].values, test_pred)\n","plt.figure()\n","plt.imshow(cm, cmap='Blues')\n","plt.title(\"Matriz de Confusão - Baseline (Teste)\")\n","plt.xlabel(\"Predito\")\n","plt.ylabel(\"Verdadeiro\")\n","for i in range(cm.shape[0]):\n"," for j in range(cm.shape[1]):\n"," plt.text(j, i, cm[i, j], ha=\"center\", va=\"center\")\n","plt.show()\n","\n","print(\"\\nRelatório de Classificação (Teste):\\n\")\n","print(classification_report(df_test[\"label\"].values, test_pred))\n"]},{"cell_type":"code","execution_count":5,"id":"fbdd4c7a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fbdd4c7a","executionInfo":{"status":"ok","timestamp":1762970227050,"user_tz":180,"elapsed":39517,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"ef0d3d4f-e530-4400-b827-f6f40168e56c"},"outputs":[{"output_type":"stream","name":"stdout","text":["RF Val Acc: 0.80375 | Val F1: 0.8037507154949158\n","RF Test Acc: 0.8166666666666667 | Test F1: 0.8166639777670223\n"]}],"source":["\n","# @title Comparativo rápido: TF-IDF + RandomForest\n","rf_pipe = Pipeline([\n"," (\"tfidf\", TfidfVectorizer(max_features=30000, ngram_range=(1,1))),\n"," (\"rf\", RandomForestClassifier(n_estimators=200, random_state=SEED, n_jobs=-1))\n","])\n","\n","rf_pipe.fit(train_text, train_y)\n","rf_val = rf_pipe.predict(val_text)\n","rf_test = rf_pipe.predict(df_test[\"text\"].values)\n","\n","print(\"RF Val Acc:\", accuracy_score(val_y, rf_val), \" | Val F1:\", f1_score(val_y, rf_val, average=\"weighted\"))\n","print(\"RF Test Acc:\", accuracy_score(df_test[\"label\"].values, rf_test), \" | Test F1:\", f1_score(df_test[\"label\"].values, rf_test, average=\"weighted\"))\n"]},{"cell_type":"markdown","id":"02952330","metadata":{"id":"02952330"},"source":["\n","> **Experimente:** \n","> - Aumente/diminua `max_features` do TF-IDF. \n","> - Troque Regressão Logística por SVM (`LinearSVC`). \n","> - Compare overfitting entre ML tradicional e DL. \n",">\n","> **Suas observações:** *(escreva abaixo)*\n"]},{"cell_type":"markdown","id":"22ba8a44","metadata":{"id":"22ba8a44"},"source":["\n","## 3) Deep Learning com LSTM (PyTorch)\n","\n","Vamos construir um pipeline enxuto com **tokenização simples**, **vocab** baseado no treino e uma **LSTM** para classificação binária. \n","> Para resultados de SOTA, considere **transformers** (BERT, DistilBERT). Aqui focamos nos fundamentos.\n"]},{"cell_type":"code","execution_count":6,"id":"3b9994fb","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3b9994fb","executionInfo":{"status":"ok","timestamp":1762970247215,"user_tz":180,"elapsed":283,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"9c3d6a00-bf75-473a-a7df-e7a21adc9de8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30000, 80, 128)"]},"metadata":{},"execution_count":6}],"source":["\n","# @title Tokenização simples + Dataset/Dataloader\n","import re\n","from collections import Counter\n","\n","def basic_tokenize(text):\n"," # minuscula, remove caracteres não alfabéticos exceto apóstrofos básicos, separa por espaços\n"," text = text.lower()\n"," text = re.sub(r\"[^a-z0-9' ]+\", \" \", text)\n"," return text.split()\n","\n","# constrói vocabulário a partir do treino\n","MAX_VOCAB = 30000\n","counter = Counter()\n","for t in train_text:\n"," counter.update(basic_tokenize(t))\n","most_common = counter.most_common(MAX_VOCAB - 2) # reserva para PAD/UNK\n","itos = [\"\", \"\"] + [w for w,_ in most_common]\n","stoi = {w:i for i,w in enumerate(itos)}\n","\n","def encode(tokens, max_len=80):\n"," ids = [stoi.get(tok, 1) for tok in tokens] # 1 = \n"," if len(ids) < max_len:\n"," ids = ids + [0] * (max_len - len(ids)) # 0 = \n"," else:\n"," ids = ids[:max_len]\n"," return np.array(ids, dtype=np.int64)\n","\n","MAX_LEN = 80\n","\n","class SentimentDataset(Dataset):\n"," def __init__(self, texts, labels):\n"," self.texts = texts\n"," self.labels = labels\n"," def __len__(self):\n"," return len(self.texts)\n"," def __getitem__(self, idx):\n"," x = encode(basic_tokenize(self.texts[idx]), MAX_LEN)\n"," y = int(self.labels[idx])\n"," return torch.tensor(x), torch.tensor(y)\n","\n","train_ds = SentimentDataset(train_text, train_y)\n","val_ds = SentimentDataset(val_text, val_y)\n","test_ds = SentimentDataset(df_test[\"text\"].values, df_test[\"label\"].values)\n","\n","BATCH_SIZE = 128\n","train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)\n","val_dl = DataLoader(val_ds, batch_size=BATCH_SIZE)\n","test_dl = DataLoader(test_ds, batch_size=BATCH_SIZE)\n","\n","len(itos), MAX_LEN, BATCH_SIZE\n"]},{"cell_type":"code","execution_count":7,"id":"71d27538","metadata":{"id":"71d27538","executionInfo":{"status":"ok","timestamp":1762970256057,"user_tz":180,"elapsed":6239,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}}},"outputs":[],"source":["\n","# @title Modelo LSTM\n","class LSTMClassifier(nn.Module):\n"," def __init__(self, vocab_size, embed_dim=128, hidden_dim=128, num_classes=2, num_layers=1, dropout=0.2):\n"," super().__init__()\n"," self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)\n"," self.lstm = nn.LSTM(embed_dim, hidden_dim, num_layers=num_layers, batch_first=True, dropout=dropout if num_layers>1 else 0.0)\n"," self.dropout = nn.Dropout(dropout)\n"," self.fc = nn.Linear(hidden_dim, num_classes)\n"," def forward(self, x):\n"," emb = self.embedding(x)\n"," out, _ = self.lstm(emb)\n"," h = out[:, -1, :]\n"," h = self.dropout(h)\n"," return self.fc(h)\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model = LSTMClassifier(vocab_size=len(itos)).to(device)\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n","\n","EPOCHS = 4 # aumente se tiver tempo/GPU\n"]},{"cell_type":"code","execution_count":8,"id":"c639c797","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c639c797","executionInfo":{"status":"ok","timestamp":1762970263524,"user_tz":180,"elapsed":5935,"user":{"displayName":"Rodrigo Moreira dos Santos","userId":"02453320943127197527"}},"outputId":"12945f34-f293-48bf-d84d-cac7c43eaa5f"},"outputs":[{"output_type":"stream","name":"stderr","text":["Epoch 1/4: 100%|██████████| 75/75 [00:02<00:00, 36.71it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1 | Loss: 0.6912 | Val Acc: 0.5375 | Val F1: 0.5150\n","✅ Modelo LSTM salvo: lstm_sentiment_best.pt\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 2/4: 100%|██████████| 75/75 [00:01<00:00, 59.83it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 2 | Loss: 0.6679 | Val Acc: 0.5817 | Val F1: 0.5667\n","✅ Modelo LSTM salvo: lstm_sentiment_best.pt\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 3/4: 100%|██████████| 75/75 [00:00<00:00, 81.81it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 3 | Loss: 0.6468 | Val Acc: 0.5354 | Val F1: 0.5129\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 4/4: 100%|██████████| 75/75 [00:00<00:00, 84.28it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 4 | Loss: 0.6538 | Val Acc: 0.5421 | Val F1: 0.5147\n"]}],"source":["\n","# @title Treino simples + validação\n","def evaluate(model, loader):\n"," model.eval()\n"," ys, ps = [], []\n"," with torch.no_grad():\n"," for xb, yb in loader:\n"," xb, yb = xb.to(device), yb.to(device)\n"," logits = model(xb)\n"," pred = torch.argmax(logits, dim=1)\n"," ys.append(yb.cpu().numpy())\n"," ps.append(pred.cpu().numpy())\n"," ys = np.concatenate(ys)\n"," ps = np.concatenate(ps)\n"," return accuracy_score(ys, ps), f1_score(ys, ps, average=\"weighted\")\n","\n","best_val = 0.0\n","for epoch in range(1, EPOCHS+1):\n"," model.train()\n"," total_loss = 0.0\n"," for xb, yb in tqdm(train_dl, desc=f\"Epoch {epoch}/{EPOCHS}\"):\n"," xb, yb = xb.to(device), yb.to(device)\n"," optimizer.zero_grad()\n"," logits = model(xb)\n"," loss = criterion(logits, yb)\n"," loss.backward()\n"," optimizer.step()\n"," total_loss += loss.item()\n"," val_acc, val_f1 = evaluate(model, val_dl)\n"," print(f\"Epoch {epoch} | Loss: {total_loss/len(train_dl):.4f} | Val Acc: {val_acc:.4f} | Val F1: {val_f1:.4f}\")\n"," if val_acc > best_val:\n"," best_val = val_acc\n"," torch.save({\n"," \"model_state\": model.state_dict(),\n"," \"vocab\": itos,\n"," \"max_len\": MAX_LEN\n"," }, \"lstm_sentiment_best.pt\")\n"," print(\"✅ Modelo LSTM salvo: lstm_sentiment_best.pt\")\n"]},{"cell_type":"code","execution_count":null,"id":"b44eb2e8","metadata":{"id":"b44eb2e8"},"outputs":[],"source":["\n","# @title Avaliação no conjunto de teste\n","# Carrega melhor checkpoint (se houver)\n","if os.path.exists(\"lstm_sentiment_best.pt\"):\n"," ckpt = torch.load(\"lstm_sentiment_best.pt\", map_location=device)\n"," model.load_state_dict(ckpt[\"model_state\"])\n","\n","test_acc, test_f1 = evaluate(model, test_dl)\n","print(\"LSTM Test Accuracy:\", test_acc)\n","print(\"LSTM Test F1:\", test_f1)\n"]},{"cell_type":"markdown","id":"7b866b6f","metadata":{"id":"7b866b6f"},"source":["\n","## 4) Exportação de Artefatos\n","\n","Vamos salvar:\n","- Pipeline TF-IDF + Regressão Logística (`baseline_pipe.pkl`)\n","- Modelo LSTM (`lstm_sentiment_best.pt`) + vocabulário embutido no checkpoint\n","\n","Esses arquivos serão usados no **deploy** (Gradio + Hugging Face Spaces).\n"]},{"cell_type":"code","execution_count":null,"id":"ccf5e781","metadata":{"id":"ccf5e781"},"outputs":[],"source":["\n","# @title Salvar pipeline baseline\n","joblib.dump(baseline_pipe, \"baseline_pipe.pkl\")\n","print(\"✅ Pipeline baseline salvo como baseline_pipe.pkl\")\n","\n","# O LSTM já foi salvo como lstm_sentiment_best.pt durante o treino (melhor época).\n","print(\"✅ Verifique se lstm_sentiment_best.pt foi gerado na etapa anterior.\")\n"]},{"cell_type":"markdown","id":"f5d63f93","metadata":{"id":"f5d63f93"},"source":["\n","## 5) Demonstração com Gradio (local)\n","\n","Abaixo, uma interface mínima com **Gradio**. Para publicar no **Hugging Face Spaces**, usaremos o arquivo `app.py` (já pronto e salvo ao lado deste notebook).\n"]},{"cell_type":"code","execution_count":null,"id":"7efbc3cc","metadata":{"id":"7efbc3cc"},"outputs":[],"source":["\n","# @title Demo local (opcional)\n","# Para executar no notebook, descomente:\n","# import gradio as gr\n","# import torch\n","# import joblib\n","\n","# # Carregar baseline (mais leve para demo)\n","# baseline = joblib.load(\"baseline_pipe.pkl\")\n","\n","# def predict_sentiment(text):\n","# proba = baseline.predict_proba([text])[0]\n","# pred = int(np.argmax(proba))\n","# label = \"positivo\" if pred == 1 else \"negativo\"\n","# conf = float(np.max(proba))\n","# return {\"predição\": label, \"confiança\": conf}\n","\n","# demo = gr.Interface(fn=predict_sentiment,\n","# inputs=gr.Textbox(label=\"Digite uma avaliação\"),\n","# outputs=gr.JSON(label=\"Resultado\"),\n","# title=\"Análise de Sentimentos (Baseline)\")\n","# demo.launch()\n","print(\"ℹ️ Use o app.py para deploy no Hugging Face Spaces.\")\n"]},{"cell_type":"markdown","id":"c8454fcd","metadata":{"id":"c8454fcd"},"source":["\n","## 6) Conclusões & Próximos Passos\n","\n","- Comparamos **ML tradicional** (TF-IDF + LR/RF) com uma **LSTM** simples. \n","- Para melhores resultados, considere **transformers** (ex.: `distilbert-base-uncased` com `transformers`). \n","- Faça *tuning* de hiperparâmetros (LR, batch size, epochs, max_features, max_len). \n","- Documente no **Relatório**: escolhas, resultados, limitações e próximos passos.\n","\n","> **Checklist para o Deploy** \n","> - `baseline_pipe.pkl` e/ou `lstm_sentiment_best.pt` gerados \n","> - `app.py` pronto (fornecido) \n","> - `requirements.txt` pronto (fornecido) \n","> - Criar o **Space** no Hugging Face (template Gradio/Python) e subir os arquivos \n","> - Preencher o `README.md` com prints e explicações\n"]}],"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 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