import gradio as gr
import torch
import torch.nn.functional as F
from transformers import Blip2Processor, Blip2ForConditionalGeneration
from PIL import Image
from peft import LoraConfig, get_peft_model


# Initialize the processor and model
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
# model_path = "full-blip2-deit-config-yes-no-2.pth"
# model = torch.load("./full-blip2-deit-config-2.pth")
# model = torch.load("./full-blip2-deit.pth") # not working - error
model = torch.load("./full-blip2-deit-config-free-form-4-ver-2.pth")

model.eval()  # Set the model to evaluation mode
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def preprocess_image(image):
    """Preprocess the image to match the model's input requirements."""
    # Convert PIL image to tensor
    pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)

    # Apply specific model's preprocessing
    patch_embeddings = model.vision_model.embeddings.patch_embeddings.projection(pixel_values)
    patch_embeddings_flat = patch_embeddings.view(1, -1, 1408)

    cls_token = model.vision_model.embeddings.cls_token.expand(1, -1, -1)
    dist_token = model.vision_model.embeddings.distillation_token.expand(1, -1, -1)
    full_embeddings = torch.cat([cls_token, dist_token, patch_embeddings_flat], dim=1)

    encoder_outputs = model.vision_model.encoder(full_embeddings)
    image_outputs = encoder_outputs.last_hidden_state

    image_outputs = F.adaptive_avg_pool2d(image_outputs, (3, 50176))
    image_outputs = image_outputs.view(1, 3, 224, 224)  # Adjusted dimensions
    return image_outputs

def generate_answer_blip2(image, question):
    """Generate answers based on an image and a question using a BLIP2 model."""
    image_outputs = preprocess_image(image)
    
    # Prepare question
    question_formatted = "Question: " + question + " Answer:"
    inputs = processor(text=question_formatted, return_tensors="pt")
    inputs['pixel_values'] = image_outputs.to(device)  # Ensure image tensor is on the correct device
    
    # Generate response using the model
    generated_ids = model.generate(**inputs, max_length=50)
    generated_answer = processor.batch_decode(generated_ids, skip_special_tokens=True)

    return generated_answer[0]  # Return the first (and typically only) generated answer

# Setting up the Gradio interface
iface = gr.Interface(
    fn=generate_answer_blip2,
    inputs=[gr.Image(label="Upload Image"), gr.Textbox(label="Enter your question")],
    outputs=gr.Textbox(label="Generated Answer"),
    title="Visual Question Answering with DeiT-BLIP2 Model",
    description="Upload an image and type a related question to receive an answer generated by the model."
)

if __name__ == "__main__":
    iface.launch()