Update app.py

app.py

@@ -1,20 +1,15 @@
 
 import gradio as gr
 import peft
-from peft import LoraConfig, PeftModel
-from transformers import AutoTokenizer, AutoModelForCausalLM, CLIPVisionModel, AutoProcessor
+from peft import LoraConfig
+from transformers import AutoTokenizer,BitsAndBytesConfig, AutoModelForCausalLM, CLIPVisionModel, AutoProcessor
 import torch
-from PIL import Image
-import requests
-import numpy as np
+from peft import PeftModel
 import torch.nn as nn
 import whisperx
-import ffmpeg, pydub
-from pydub import AudioSegment
 
 clip_model_name = "wkcn/TinyCLIP-ViT-61M-32-Text-29M-LAION400M"
 phi_model_name  = "microsoft/phi-2"
-
 tokenizer  = AutoTokenizer.from_pretrained(phi_model_name, trust_remote_code=True)
 processor  = AutoProcessor.from_pretrained(clip_model_name)
 tokenizer.pad_token = tokenizer.eos_token
@@ -22,41 +17,49 @@ IMAGE_TOKEN_ID = 23893 # token for word comment
 device = "cuda" if torch.cuda.is_available() else "cpu"
 clip_embed = 640
 phi_embed  = 2560
-compute_type = "float32"
-audio_batch_size = 1
-
-import gc
-
+compute_type = "float16"
+audio_batch_size = 16
+
+class SimpleResBlock(nn.Module):
+    def __init__(self, phi_embed):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(phi_embed)
+        self.proj = nn.Sequential(
+            nn.Linear(phi_embed, phi_embed),
+            nn.GELU(),
+            nn.Linear(phi_embed, phi_embed)
+        )
+    def forward(self, x):
+        x = self.pre_norm(x)
+        return x + self.proj(x)
+        
 # models
 clip_model = CLIPVisionModel.from_pretrained(clip_model_name).to(device)
-
 projection = torch.nn.Linear(clip_embed, phi_embed).to(device)
-
-gc.collect()
-phi_model = AutoModelForCausalLM.from_pretrained(
-    phi_model_name,
-    trust_remote_code=True,
-     )
-audio_model = whisperx.load_model("small", device, compute_type=compute_type)
+resblock = SimpleResBlock(phi_embed).to(device)
+phi_model = AutoModelForCausalLM.from_pretrained(phi_model_name,trust_remote_code=True).to(device)
+audio_model = whisperx.load_model("tiny", device, compute_type=compute_type)
 
 # load weights
-model_to_merge = PeftModel.from_pretrained(phi_model,'./model_chkpt/')
-merged_model = model_to_merge.merge_and_unload().to(device)
-projection.load_state_dict(torch.load('./ft_projection.pth',map_location=torch.device(device)))
+model_to_merge = PeftModel.from_pretrained(phi_model,'./model_chkpt/qlora_adaptor')
+merged_model   = model_to_merge.merge_and_unload()
+projection.load_state_dict(torch.load('./model_chkpt/ft_projection_layer.pth',map_location=torch.device(device)))
+resblock.load_state_dict(torch.load('./model_chkpt/ft_projection_model.pth',map_location=torch.device(device)))
 
-def inference(img=None,img_audio=None,val_q=None):
+def model_generate_ans(img=None,img_audio=None,val_q=None):
 
-    max_generate_length = 50
+    max_generate_length = 100
     val_combined_embeds = []
-
+    
     with torch.no_grad():
-
+    
         # image
         if img is not None:
             image_processed  = processor(images=img, return_tensors="pt").to(device)
             clip_val_outputs = clip_model(**image_processed).last_hidden_state[:,1:,:]
             val_image_embeds = projection(clip_val_outputs)
-
+            val_image_embeds = resblock(val_image_embeds).to(torch.float16)
+            
             img_token_tensor = torch.tensor(IMAGE_TOKEN_ID).to(device)
             img_token_embeds = merged_model.model.embed_tokens(img_token_tensor).unsqueeze(0).unsqueeze(0)
 
@@ -65,64 +68,59 @@ def inference(img=None,img_audio=None,val_q=None):
 
         # audio
         if img_audio is not None:
-
-            # accepting only initial few secs speech
-            audio = AudioSegment.from_mp3( img_audio)
-            clipped_audio = audio[:20*1000] 
-            clipped_audio.export( 'audio.mp3', format="mp3")
-            result = audio_model.transcribe('audio.mp3')
+            audio_result = audio_model.transcribe(img_audio)
             audio_text = ''
-
-            audio_text = result["segments"][0]['text']
+            for seg in audio_result['segments']:
+                audio_text += seg['text']
             audio_text = audio_text.strip()
             audio_tokens = tokenizer(audio_text, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0).to(device)
             audio_embeds    = merged_model.model.embed_tokens(audio_tokens).unsqueeze(0)
             val_combined_embeds.append(audio_embeds)
-
+            
         # text question
         if len(val_q) != 0:
             val_q_tokenised = tokenizer(val_q, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0).to(device)
             val_q_embeds    = merged_model.model.embed_tokens(val_q_tokenised).unsqueeze(0)
             val_combined_embeds.append(val_q_embeds)
 
-        # val_combined_emb
         val_combined_embeds = torch.cat(val_combined_embeds,dim=1)
-
+        
+        #val_combined_embeds = torch.cat([val_image_embeds, img_token_embeds, val_q_embeds], dim=1) # 4, 69, 2560
         predicted_caption = torch.full((1,max_generate_length),50256).to(device)
-
+    
         for g in range(max_generate_length):
-            phi_output_logits = merged_model(inputs_embeds=val_combined_embeds)['logits'] 
-            predicted_word_token_logits = phi_output_logits[:, -1, :].unsqueeze(1) 
-            predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1) 
+            phi_output_logits = merged_model(inputs_embeds=val_combined_embeds)['logits'] # 4, 69, 51200
+            predicted_word_token_logits = phi_output_logits[:, -1, :].unsqueeze(1) # 4,1,51200
+            predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1) # 4,1
             predicted_caption[:,g] = predicted_word_token.view(1,-1)
-            next_token_embeds = phi_model.model.embed_tokens(predicted_word_token) 
+            next_token_embeds = phi_model.model.embed_tokens(predicted_word_token) # 4,1,2560
             val_combined_embeds   = torch.cat([val_combined_embeds, next_token_embeds], dim=1)
-
+            
         predicted_captions_decoded = tokenizer.batch_decode(predicted_caption,ignore_index = 50256)[0]
-
+    
     return predicted_captions_decoded
+    
 
 with gr.Blocks() as demo:
 
     gr.Markdown(
     """
-    # multi-modalLLM
-    Build using Tiny Clip model and Microsoft's Phi-2 model fine tuned on Instruct 150k.
+    # Chat with MultiModal GPT !
+    Build using combining clip model and phi-2 model.
     """
     )
 
     # app GUI
     with gr.Row():
         with gr.Column():
-            img_input    = gr.Image(label='Reference Image',type="pil")
-            img_question = gr.Text(label ='Question related to Image')
-            img_audio    = gr.Audio(label="Speak a question", sources=['microphone', 'upload'], type='filepath')            
+            img_input    = gr.Image(label='Image',type="pil")
+            img_audio    = gr.Audio(label="Audio Query", sources=['microphone', 'upload'], type='filepath')
+            img_question = gr.Text(label ='Text Query')
         with gr.Column():
-            img_answer   = gr.Text(label ='Response')
-
-    section_btn = gr.Button("Process")
-    section_btn.click(inference, inputs=[img_input,img_audio,img_question], outputs=[img_answer])
+            img_answer   = gr.Text(label ='Answer')
 
+    section_btn = gr.Button("Submit")
+    section_btn.click(model_generate_ans, inputs=[img_input,img_audio,img_question], outputs=[img_answer])
+    
 if __name__ == "__main__":
-    demo.launch(debug=True)
-
+    demo.launch()