import gradio as gr
from PIL import Image
import clipGPT
import vitGPT
import skimage.io as io
import PIL.Image
import difflib
import ViTCoAtt
import cnnrnn
from build_vocab import Vocabulary


    

# Caption generation functions
def generate_caption_clipgpt(image, max_tokens, temperature):
    caption = clipGPT.generate_caption_clipgpt(image, max_tokens, temperature)
    return caption

def generate_caption_vitgpt(image, max_tokens, temperature):
    caption = vitGPT.generate_caption(image, max_tokens, temperature)
    return caption

def generate_caption_vitCoAtt(image):
    caption = ViTCoAtt.CaptionSampler.main(image)
    return caption

def generate_caption_cnnrnn(image):
    # with open('/content/Image_features_ecoder_decoder.pickle', 'rb') as f:
        # Xnet_features = pickle.load(f)
        # image = Xnet_features[image]
        # caption = cnn-rnn.get_result(image)
        caption = ""
        return caption


with gr.Row():
     
    image = gr.Image(label="Upload Chest X-ray", type="pil")   
                     
       
with gr.Row():
 
    with gr.Column(): # Column for dropdowns and model choice
        max_tokens = gr.Dropdown(list(range(50, 101)), label="Max Tokens", value=75)
        temperature = gr.Slider(0.5, 0.9, step=0.1, label="Temperature", value=0.7) 

    model_choice = gr.Radio(["CLIP-GPT2", "ViT-GPT2", "ViT-CoAttention"], label="Select Model") 
    generate_button = gr.Button("Generate Caption") 
    
    
caption = gr.Textbox(label="Generated Caption") 
    
def predict(img, model_name, max_tokens, temperature):
    if model_name == "CLIP-GPT2":
      return generate_caption_clipgpt(img, max_tokens, temperature)
    elif model_name == "ViT-GPT2":
      return generate_caption_vitgpt(img, max_tokens, temperature)
    elif model_name == "ViT-CoAttention":
      return generate_caption_vitCoAtt(img)
    elif model_name == "Baseline Model CNN-RNN":
        print(img)
      return generate_caption_cnnrnn(img)
    else:
      return "Caption generation for this model is not yet implemented."   



examples = [[f"example{i}.jpg"] for i in range(1,4)]

description= "You can generate captions by uploading an X-Ray and selecting a model of your choice below. Please select the number of Max Tokens and Temperature setting, if you are testing CLIP GPT2 and VIT GPT2 Models"
title = "MedViT: A Vision Transformer-Driven Method for Generating Medical Reports 🏥🤖"

interface = gr.Interface(
        fn=predict,
        inputs = [image, model_choice, max_tokens, temperature],
        theme="soft",
        outputs=caption,
        examples = examples,
        title = title,
        description = description
    )

interface.launch(debug=True)