app.py

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)