Create app.py

app.py

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+
+import gradio as gr
+import peft
+from peft import LoraConfig, PeftModel
+from transformers import AutoTokenizer, AutoModelForCausalLM, CLIPVisionModel, AutoProcessor
+import torch
+from PIL import Image
+import requests
+import numpy as np
+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
+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
+
+# 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)
+
+# 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)))
+
+def inference(img=None,img_audio=None,val_q=None):
+
+    max_generate_length = 50
+    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)
+
+            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)
+
+            val_combined_embeds.append(val_image_embeds)
+            val_combined_embeds.append(img_token_embeds)
+
+        # 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_text = ''
+
+            audio_text = result["segments"][0]['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)
+
+        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) 
+            predicted_caption[:,g] = predicted_word_token.view(1,-1)
+            next_token_embeds = phi_model.model.embed_tokens(predicted_word_token) 
+            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.
+    """
+    )
+
+    # 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')            
+        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])
+
+if __name__ == "__main__":
+    demo.launch(debug=True)
+