app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer
from threading import Thread
import re
import time
from PIL import Image
import torch
import spaces
#import subprocess
#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)


processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
model = AutoModelForVision2Seq.from_pretrained("HuggingFaceTB/SmolVLM-Instruct", 
        torch_dtype=torch.bfloat16,
        #_attn_implementation="flash_attention_2"
        )

def model_inference(
    input_dict, history, decoding_strategy, temperature, max_new_tokens,
    repetition_penalty, top_p
): 
    text = input_dict["text"]
    print(input_dict["files"])
    if len(input_dict["files"]) > 1:
      images = [Image.open(image).convert("RGB") for image in input_dict["files"]]
    elif len(input_dict["files"]) == 1:
      images = [Image.open(input_dict["files"][0]).convert("RGB")]        
    

    if text == "" and not images:
        gr.Error("Please input a query and optionally image(s).")

    if text == "" and images:
        gr.Error("Please input a text query along the image(s).")

    


    resulting_messages = [
                {
                    "role": "user",
                    "content": [{"type": "image"} for _ in range(len(images))] + [
                        {"type": "text", "text": text}
                    ]
                }
            ]
    prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[images], return_tensors="pt")
    inputs = {k: v.to("cuda") for k, v in inputs.items()}
    generation_args = {
        "max_new_tokens": max_new_tokens,
        "repetition_penalty": repetition_penalty,

    }

    assert decoding_strategy in [
        "Greedy",
        "Top P Sampling",
    ]
    if decoding_strategy == "Greedy":
        generation_args["do_sample"] = False
    elif decoding_strategy == "Top P Sampling":
        generation_args["temperature"] = temperature
        generation_args["do_sample"] = True
        generation_args["top_p"] = top_p

    generation_args.update(inputs)
    # Generate
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens= True)
    generation_args = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
    generated_text = ""

    thread = Thread(target=model.generate, kwargs=generation_args)
    thread.start()
    thread.join()
    
    buffer = ""
    
      
    for new_text in streamer:
    
      buffer += new_text
      generated_text_without_prompt = buffer#[len(ext_buffer):]
      time.sleep(0.01)
      yield buffer


demo = gr.ChatInterface(fn=model_inference, title="Geoscience AI Interpreter", 
                description="This app take the thin sections, seismic images etc. and interpret them. You just upload an image and text along with it. It works best with single turn conversations, so clear the conversation after a single turn.",
                textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True, 
                        additional_inputs=[gr.Radio(["Top P Sampling",
              "Greedy"],
          value="Greedy",
          label="Decoding strategy",
          #interactive=True,
          info="Higher values is equivalent to sampling more low-probability tokens.",
                                                  
      ), gr.Slider(
          minimum=0.0,
          maximum=5.0,
          value=0.4,
          step=0.1,
          interactive=True,
          label="Sampling temperature",
          info="Higher values will produce more diverse outputs.",
      ),
                                            gr.Slider(
          minimum=8,
          maximum=1024,
          value=512,
          step=1,
          interactive=True,
          label="Maximum number of new tokens to generate",
      ), gr.Slider(
          minimum=0.01,
          maximum=5.0,
          value=1.2,
          step=0.01,
          interactive=True,
          label="Repetition penalty",
          info="1.0 is equivalent to no penalty",
      ), 
         gr.Slider(
          minimum=0.01,
          maximum=0.99,
          value=0.8,
          step=0.01,
          interactive=True,
          label="Top P",
          info="Higher values is equivalent to sampling more low-probability tokens.",
      )],cache_examples=False
                )
     
      
      

demo.launch(debug=True)