Upload folder using huggingface_hub

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto
+ml1m/content/dataset/ratings.dat filter=lfs diff=lfs merge=lfs -text
+multimodel.pt filter=lfs diff=lfs merge=lfs -text

.gitignore

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+safetensors/target
+safetensors/**/Cargo.lock
+bindings/python/Cargo.lock
+*.bin
+*.h5
+*.msgpack
+*.pt
+*.pdparams
+*.safetensors
+*.npz

ML-MovieGenre.code-workspace

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+{
+	"folders": [
+		{
+			"path": "."
+		},
+	],
+	"settings": {}
+}

README.md

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 ---
-title: Movie-Genres-Multilabel MultiPoro
-emoji: 🚀
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.12.0
+title: Movie-Genres-Multilabel_MultiPoro
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.48.0
 ---
+# Poro 2.0: Title-only Sentiment Analysis
+
+## Introduction
+A simple title-only sentiment analysis model using a distilled version of BERT model. The model is trained on the MovieLens1M dataset and achieves a multi-label F1 score of 0.2733 on macro-average and 0.4443 on micro-average.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Authors
+- Duy Dang - <dduy193.cs@gmail.com>
+- Chien Nguyen - <duychien.work@gmail.com>

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import torch
+import pandas as pd
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+import gradio as gr
+import warnings
+import streamlit as st
+from PIL import Image
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, DistilBertForSequenceClassification, AutoModelForSeq2SeqLM
+from tqdm import tqdm
+from torchvision import models
+from torchvision.transforms import v2
+from torch.utils.data import Dataset, DataLoader
+from keras.preprocessing import image
+from torchmetrics.classification import MultilabelF1Score
+from sklearn.metrics import average_precision_score, ndcg_score
+
+
+# In[2]:
+
+
+warnings.filterwarnings("ignore")
+
+
+# In[3]:
+
+
+genres = ["Crime", "Thriller", "Fantasy", "Horror", "Sci-Fi", "Comedy", "Documentary", "Adventure", "Film-Noir", "Animation", "Romance", "Drama", "Western", "Musical", "Action", "Mystery", "War", "Children\'s"]
+mapping = {}
+for i in range(len(genres)):
+    mapping[i] = genres[i]
+mapping
+
+
+# In[4]:
+
+
+tokenizer_gen = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
+model_gen = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
+
+tokenizer1 = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model1 = DistilBertForSequenceClassification .from_pretrained("distilbert-base-uncased", problem_type="multi_label_classification", num_labels=18)
+model1.config.id2label = mapping
+
+tokenizer2 = AutoTokenizer.from_pretrained("dduy193/plot-classification")
+model2 = AutoModelForSequenceClassification.from_pretrained("dduy193/plot-classification")
+model2.config.id2label = mapping
+
+model3 = models.resnet101(pretrained=False)
+model3.fc = torch.nn.Linear(2048, len(genres))
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model1.to(device)
+model2.to(device)
+model3.to(device)
+model_gen.to(device)
+device
+
+
+# In[5]:
+
+
+class Multimodal(torch.nn.Module):
+    def __init__(self, model1, model2, model3):
+        super().__init__()
+        self.model1 = model1
+        self.model2 = model2
+        self.model3 = model3
+        self.fc1 = torch.nn.Linear(18, 18)
+        self.fc2 = torch.nn.Linear(18, 18)
+        self.fc3 = torch.nn.Linear(18, 18)
+
+    def forward(self, 
+                title_input_ids, title_attention_mask,
+                plot_input_ids, plot_attention_mask,
+                image_input):
+        title_output = self.model1(title_input_ids, title_attention_mask)
+        plot_output = self.model2(plot_input_ids, plot_attention_mask)
+        image_output = self.model3(image_input)
+
+        title_output = self.fc1(title_output.logits)
+        plot_output = self.fc2(plot_output.logits)
+        image_output = self.fc3(image_output)
+        
+        output = torch.add(title_output, plot_output)
+        output = torch.add(output, image_output)
+        return output
+
+
+# In[6]:
+
+
+model = Multimodal(model1, model2, model3)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+device
+
+
+# **_PLEASE INSTALL THE MODEL CHECKPOINT FROM THE LINK IN README.txt_**
+
+# In[7]:
+
+
+model.load_state_dict(torch.load('multimodel.pt'))
+model.eval()
+
+
+# In[8]:
+
+
+def generate_plot(title: str, model: AutoModelForSeq2SeqLM, tokenizer: AutoTokenizer, device) -> str:
+    quote = 'What is the story of the movie {}?'
+    model_gen.to(device)
+    model_gen.eval()
+
+    input_ids = tokenizer(quote.format(title), return_tensors='pt').input_ids.to(device)
+    output = model.generate(input_ids, max_length=256, do_sample=True, temperature=0.09)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+
+# In[9]:
+
+
+def inference(title, image, 
+              tokenizer1=tokenizer1, tokenizer2=tokenizer2, tokenizer_gen=tokenizer_gen,
+              model_gen=model_gen, model=model, 
+              genres=genres, device=device):
+    title_input = tokenizer1(title, return_tensors='pt', padding=True, truncation=True)
+    title_input_ids = title_input['input_ids'].to(device)
+    title_attention_mask = title_input['attention_mask'].to(device)
+
+    plot = generate_plot(title, model_gen, tokenizer_gen, device)
+    plot_input = tokenizer2(plot, return_tensors='pt', padding=True, truncation=True)
+    plot_input_ids = plot_input['input_ids'].to(device)
+    plot_attention_mask = plot_input['attention_mask'].to(device)
+
+    # If image is not uploaded
+    if image is None:
+        image_input = torch.zeros((1, 3, 224, 224)).to(device)
+
+    else:
+        image_input = image.resize((224, 224))
+        image_input = v2.ToTensor()(image_input)
+        image_input = image_input.unsqueeze(0)
+        image_input = image_input.to(device)
+
+    output = model(title_input_ids, title_attention_mask, plot_input_ids, plot_attention_mask, image_input)
+    output = torch.sigmoid(output)
+    output = output.cpu().detach().numpy()
+    output = np.where(output > 0.5, 1, 0)
+    output = output.squeeze()
+    output = np.where(output == 1)[0]
+    output = [genres[i] for i in output]
+    return output
+
+
+# In[10]:
+
+
+app = gr.Interface(fn=inference, inputs=["text", "pil"], outputs="text", title="Movie Genre Classification", 
+                   description="This model classifies the genre of a movie based on its title and poster.", 
+                   examples=[["The Matrix", "https://upload.wikimedia.org/wikipedia/en/c/c1/The_Matrix_Poster.jpg"],
+                             ["The Dark Knight", "https://upload.wikimedia.org/wikipedia/en/1/1c/The_Dark_Knight_%282008_film%29.jpg"],
+                             ["The Godfather", "https://upload.wikimedia.org/wikipedia/en/1/1c/Godfather_ver1.jpg"],
+                             ["The Shawshank Redemption", "https://upload.wikimedia.org/wikipedia/en/8/81/ShawshankRedemptionMoviePoster.jpg"],
+                             ["The Lord of the Rings: The Return of the King", "https://upload.wikimedia.org/wikipedia/en/2/23/The_Lord_of_the_Rings%2C_TROTK_%282003%29.jpg"],
+                             ["The Godfather: Part II", "https://upload.wikimedia.org/wikipedia/en/0/03/Godfather_part_ii.jpg"]])
+
+
+# In[11]:
+
+
+app.launch(share=True)
+

ml1m/content/dataset/genres.txt

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+Crime
+Thriller
+Fantasy
+Horror
+Sci-Fi
+Comedy
+Documentary
+Adventure
+Film-Noir
+Animation
+Romance
+Drama
+Western
+Musical
+Action
+Mystery
+War
+Children's