created app

app.py

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+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+import matplotlib.pyplot as plt
+import random
+import streamlit as st
+import requests
+from io import BytesIO
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+def prediction(model, image_file, question):
+    question = f"<image>\n{question}"
+    # set the max number of tiles in `max_num`
+    pixel_values = load_image(image_file, max_num=12).to(torch.bfloat16).cuda()
+    generation_config = dict(max_new_tokens=1024, do_sample=False)
+
+    response = model.chat(tokenizer, pixel_values, question, generation_config)
+
+    return response
+
+# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
+path = 'Ramji/slake_vqa_internvl_demo'
+intern_model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=False,
+    trust_remote_code=True).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+
+# Title of the Streamlit app
+st.title("Image VQA")
+
+# Step 1: Upload an image
+st.header("Upload an Image")
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+# Step 2: Input a question
+st.header("Ask a Question")
+question = st.text_input("Type your question here:")
+
+# Step 3: Handle the uploaded image by saving it and reading its path
+if uploaded_image is not None:
+    # Save the uploaded image to a file
+    image_path = os.path.join("uploaded_images", uploaded_image.name)
+    
+    # Make sure the directory exists
+    os.makedirs("uploaded_images", exist_ok=True)
+    
+    # Write the image to a file
+    with open(image_path, "wb") as f:
+        f.write(uploaded_image.getbuffer())
+    
+    # Read the image from the saved file path
+    image = Image.open(image_path)
+    
+    # Display the uploaded image
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    st.write(f"Image saved at: {image_path}")
+
+# Step 4: Display the typed question
+if question:
+    st.write(f"Your question: **{question}**")
+
+# Optional: Process the image and question for a VLM (like CLIP or BLIP)
+if uploaded_image and question:
+    st.write("Processing the image and question...")
+
+    output = prediction(intern_model, image_file, question)
+
+    st.write("Model output: This is where the answer will appear.")