Spaces:
Runtime error
Runtime error
File size: 4,451 Bytes
70b16bd 0833e94 738ac11 0e2cda7 738ac11 70b16bd 0833e94 70b16bd 738ac11 0833e94 738ac11 0833e94 ae9293a 0833e94 70b16bd 0833e94 738ac11 0833e94 738ac11 0833e94 70b16bd 6e8de0e a474c8c 70b16bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import numpy as np
import os
import gradio as gr
import torch
import torch.nn as nn
from PIL import Image
from transformers import CLIPModel, AutoModel
from typing import Optional
from safetensors.torch import load_model
os.environ["WANDB_DISABLED"] = "true"
from datasets import load_dataset, load_metric
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
logging,
)
class VisionTextDualEncoderModel(nn.Module):
def __init__(self, num_classes):
super(VisionTextDualEncoderModel, self).__init__()
# Load the XLM-RoBERTa model
self.text_encoder = AutoModel.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual")
# Define your vision model (e.g., using torchvision)
self.vision_encoder = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
vision_output_dim = self.vision_encoder.config.vision_config.hidden_size
# Combine the modalities
self.fc = nn.Linear(
self.text_encoder.config.hidden_size + vision_output_dim, num_classes
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
return_loss: Optional[bool] = None,
token_type_ids: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
):
# Encode text inputs
text_outputs = self.text_encoder(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
).pooler_output
# Encode vision inputs
vision_outputs = self.vision_encoder.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Concatenate text and vision features
combined_features = torch.cat(
(text_outputs, vision_outputs.pooler_output), dim=1
)
# Forward through a linear layer for classification
logits = self.fc(combined_features)
return {"logits": logits}
id2label = {0: "negative", 1: "neutral", 2: "positive"}
label2id = {"negative": 0, "neutral": 1, "positive": 2}
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual")
model = VisionTextDualEncoderModel(num_classes=3)
config = model.vision_text_model.config
# https://huggingface.co/FFZG-cleopatra/M2SA/blob/main/model.safetensors
sf_filename = hf_hub_download("FFZG-cleopatra/M2SA", filename="model.safetensors")
load_model(model,"model.safetensors") # model.load_state_dict(torch.load(model_args.model_name_or_path+"-finetuned/pytorch_model.bin"))
model = AutoModelForSequenceClassification.from_pretrained(
"FFZG-cleopatra/M2SA",
num_labels=3, id2label=id2label,
label2id=label2id
)
def predict_sentiment(text, image):
print(text, image)
text_inputs = tokenizer(
text,
max_length=512,
padding="max_length",
truncation=True,
)
image_transformations = Transform(
config.vision_config.image_size,
image_processor.image_mean,
image_processor.image_std,
)
image_transformations = torch.jit.script(image_transformations)
image = image_transformations(image)
model_input = {
"input_ids" : text_inputs.input_ids,
"pixel_values":image
"attention_mask" : text_inputs.attention_mask,
}
prediction = None
with torch.no_grad():
prediction = model(model_input)
print(prediction)
return prediction
interface = gr.Interface(
fn=lambda text, image: predict_sentiment(text, image),
inputs=[gr.inputs.Textbox(),gr.inputs.Image(shape=(224, 224))],
outputs=['text'],
title='Multilingual-Multimodal-Sentiment-Analysis',
examples= ["I love tea","I hate coffee"],
description='Get the positive/neutral/negative sentiment for the given input.'
)
interface.launch(inline = False)
|