app.py

import gradio as gr
import random
from PIL import Image
import os
import torch
from transformers import BlipProcessor, BlipForConditionalGeneration


# Load your images
image_dir = "images"
images = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".jpg")]
STATIC_IMAGE_PATH = "images/Places365_val_00000009.jpg"
# Load the model and processor
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Dummy inference function for now
def run_vlm(image, prompt):
    inputs = processor(image, prompt, return_tensors="pt").to(device)
    out = model.generate(**inputs)
    return processor.decode(out[0], skip_special_tokens=True)
# return f"Model output for: '{prompt}' and selected image."

def get_random_image(event=None):
    jpgs = [f for f in os.listdir(image_dir) if f.endswith(".jpg")]
    if not jpgs:
        return gr.update(value=STATIC_IMAGE_PATH), gr.update(value=STATIC_IMAGE_PATH)
    
    selected = os.path.join(image_dir, random.choice(jpgs))
    print(selected)
    return gr.update(value=selected), gr.update(value=selected)

        

def process(image_path, user_prompt):
    image = Image.open(image_path)
    return run_vlm(image, user_prompt)

with gr.Blocks() as demo:
    with gr.Row():
        image_display = gr.Image(value=STATIC_IMAGE_PATH, type="filepath", label="Selected Image")

    image_path = gr.Textbox(value=STATIC_IMAGE_PATH, visible=False)
    user_prompt = gr.Textbox(label="User Prompt")

    with gr.Row():
        random_button = gr.Button("Random Photo")
        run_button = gr.Button("Run Model")

    output = gr.Textbox(label="Model Output")

    random_button.click(fn=get_random_image, outputs=[image_display, image_path])
    run_button.click(fn=process, inputs=[image_path, user_prompt], outputs=output)

# with gr.Blocks() as demo:
#     with gr.Row():
#         image_display = gr.Image(type="filepath", label="Selected Image")
#         random_button = gr.Button("Randomize Photo")

#     image_path = gr.Textbox(visible=False)
#     user_prompt = gr.Textbox(label="User Prompt")
#     run_button = gr.Button("Run Model")
#     output = gr.Textbox(label="Model Output")

#     random_button.click(fn=random_image, outputs=[image_display, image_path])
#     run_button.click(fn=process, inputs=[image_path, user_prompt], outputs=output)


demo.launch()