application file

app.py

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+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()