Hugging Face's logo

Spaces:
TharunSivamani
/
Capstone
Sleeping

App Files Community
Capstone
File size: 4,943 Bytes 
5f3e0e0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
 
import torch
import torch.nn as nn
import torch.nn.functional as F
import whisperx
from transformers import  AutoTokenizer
from transformers import AutoModelForCausalLM
from transformers import CLIPVisionModel, CLIPImageProcessor
import peft
import gradio as gr 

device = 'cpu'
model_name = "microsoft/phi-2"
whisper_model = whisperx.load_model('small', device='cpu', compute_type='float32')
image_processor = CLIPImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
clip_model = CLIPVisionModel.from_pretrained('openai/clip-vit-base-patch32')

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.bos_token = tokenizer.eos_token

phi2_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True, 
    device_map = 'cpu'
)
phi2_model.config.use_cache = False


def CLIP_embeddings(image):
    _ = clip_model.requires_grad_(False) 
    image = image_processor(images=image, return_tensors="pt")
    image_out = clip_model(image['pixel_values'].to(device=clip_model.device), output_hidden_states=True)
    return features(image_out)

def embed_audio(file_name): 
    result = whisper_model.transcribe(file_name)
    res_text = ''

    for segment in result['segments']: 
        res_text = res_text + segment['text']

    return res_text.strip()

def features(image_out):
    image_features = image_out.hidden_states[-1]
    return image_features[:, 1:, :]

def embed_text(text): 
    input_tokens = tokenizer(text, return_tensors="pt", return_attention_mask=False)    
    return phi2_model.get_input_embeddings()(input_tokens.input_ids)

    
class ResBlock(nn.Module):
    def __init__(self, input_size):
        super().__init__()
        self.pre_norm = nn.LayerNorm(input_size)
        self.proj = nn.Sequential(
            nn.Linear(input_size, input_size),
            nn.GELU(),
            nn.Linear(input_size, input_size)
        )

    def forward(self, x):
        x = self.pre_norm(x)
        return x + self.proj(x)

class Projection_Model(nn.Module): 
    def __init__(
        self,  
        dim_input_CLIP = 768, 
        dim_input_Phi2 = 2560
    ):
        super(Projection_Model, self).__init__()
        self.projection_img = nn.Linear(
            dim_input_CLIP, dim_input_Phi2, bias=False
        )
        self.resblock = ResBlock(dim_input_Phi2)

    def forward(self, x): 
        x = self.projection_img(x)
        return self.resblock(x)


model = Projection_Model()
model.projection_img.load_state_dict(torch.load("projection.pth", map_location='cpu'))
model.resblock.load_state_dict(torch.load("block.pth", map_location='cpu'))


def embeddings_image(image): 
    clip_embeddings = CLIP_embeddings(image)
    return model(clip_embeddings)

user = "TharunSivamani"  
model_name = "qlora-phi2"
model_id = f"{user}/{model_name}"

phi2_model_peft = peft.PeftModel.from_pretrained(phi2_model, model_id)


def inference(image=None, audio=None, text=None):
    if len(text) == 0: 
        text = None 

    if image is None and audio is None and text is None: 
        return None
    
    context = tokenizer("Context: ", return_tensors="pt", return_attention_mask=False)
    input_embeds = phi2_model_peft.get_input_embeddings()(context.input_ids)
    
    if image is not None: 
        query = text
        image_embeds = embeddings_image(image)
        input_embeds = torch.cat((input_embeds, image_embeds), dim=1)
    
    if audio is not None: 
        audio_transcribed = embed_audio(audio)
        audio_embeds = embed_text(audio_transcribed)
        input_embeds = torch.cat((input_embeds, audio_embeds), dim=1)

    if text is not None: 
        query = text
        text_embeds = embed_text(text)
        input_embeds = torch.cat((input_embeds, text_embeds), dim=1)

    question = tokenizer(" Question: " + query, return_tensors="pt", return_attention_mask=False)
    question_embeds = phi2_model_peft.get_input_embeddings()(question.input_ids)
    
    input_embeds = torch.cat((input_embeds, question_embeds), dim=1) 

    answer = tokenizer(" Answer: ", return_tensors="pt", return_attention_mask=False)
    answer_embeds = phi2_model_peft.get_input_embeddings()(answer.input_ids)
    
    input_embeds = torch.cat((input_embeds, answer_embeds), dim=1) 
    result = phi2_model_peft.generate(inputs_embeds=input_embeds, bos_token_id = tokenizer.bos_token_id)
    final_ans = tokenizer.batch_decode(result)[0]
    final_ans = final_ans.split(tokenizer.eos_token)

    if final_ans[0] == '': 
        return final_ans[1]
    else: 
        return final_ans[0]


demo = gr.Interface(
    fn = inference,
    inputs = [
        gr.Image(label="Image Input"),
        gr.Audio(label="Audio Input", sources=["microphone", "upload"], type="filepath"),
        gr.Textbox(label="Text Input"), 
    ],
    outputs = [
        gr.Textbox(label='Answer'),
    ],
)

demo.launch()