add basic example

2025-08-04 15:27:54 +02:00
parent bbb868b357
commit c324247dc3
17 changed files with 2116 additions and 52 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,4 @@
 .jj
-.tmp-data
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[codz]
--- a/.tmp-data/coding_description
+++ b/.tmp-data/coding_description
@@ -0,0 +1,158 @@
+Here's a **Prolog program** that represents the logical rules for classifying an animal as either a **cat** or a **dog**, based on the morphological features described in your detailed comparison. The program uses a set of predicates representing visual traits, and applies a decision framework to infer classification.
+
+This version is designed to work with facts (trait observations) and logical inference using rules.
+
+---
+
+### ✅ Prolog Program: Animal Classifier
+
+```prolog
+% -------------------------------
+% TRAIT PREDICATES
+% These represent observable features from an image.
+% Each predicate returns true if the trait is present in the animal.
+% -------------------------------
+
+% Skull traits
+is_short_rounded_skull :- true.     % Assume this is observed (can be set based on image input)
+is_flat_face :- true.
+
+is_long_elongated_skull :- false.
+is_prominent_muzzle :- false.
+
+% Ear traits
+is_tall_pointed_ear :- true.
+is_high_set_ear :- true.
+
+is_floppy_or_rounded_ear :- false.
+is_wide_spaced_ear :- false.
+
+% Eye traits
+is_large_round_eye :- true.
+is_lateral_eye_position :- true.
+
+is_small_eye :- false.
+is_forward_facing_eye :- false.
+
+% Paw traits
+is_small_paw_with_visible_toes :- true.
+has_retractable_claws :- true.
+
+is_large_paw_with_non_retractable_claws :- false.
+
+% Tail traits
+is_long_thin_tail :- true.
+is_tail_held_high_or_curled :- true.
+
+is_thick_tail :- false.
+
+% Body posture traits
+is_low_to_ground_body :- true.
+is_compact_body :- true.
+
+is_robust_upright_body :- false.
+is_long_legs_relative_to_body :- false.
+
+% Muzzle traits (profile)
+has_visible_stop_in_profile :- false.
+has_overbite_or_underbite :- false.
+
+% -------------------------------
+% CLASSIFICATION RULES
+% These are based on the decision framework provided.
+% -------------------------------
+
+% Cat classification rules
+is_cat :-
+    cat_traits_count(CatCount),
+    dog_traits_count(DogCount),
+    CatCount >= 3,
+    DogCount =:= 0.
+
+% Dog classification rules
+is_dog :-
+    cat_traits_count(CatCount),
+    dog_traits_count(DogCount),
+    DogCount >= 3,
+    CatCount =:= 0.
+
+% Counting cat-specific traits
+cat_traits_count(Count) :-
+    findall(Trait, cat_trait(Trait), Traits),
+    length(Traits, Count).
+
+% Define which predicates are cat-specific traits
+cat_trait(is_short_rounded_skull).
+cat_trait(is_flat_face).
+cat_trait(is_tall_pointed_ear).
+cat_trait(is_high_set_ear).
+cat_trait(is_large_round_eye).
+cat_trait(is_lateral_eye_position).
+cat_trait(is_small_paw_with_visible_toes).
+cat_trait(has_retractable_claws).
+cat_trait(is_long_thin_tail).
+cat_trait(is_tail_held_high_or_curled).
+cat_trait(is_low_to_ground_body).
+cat_trait(is_compact_body).
+
+% Counting dog-specific traits
+dog_traits_count(Count) :-
+    findall(Trait, dog_trait(Trait), Traits),
+    length(Traits, Count).
+
+% Define which predicates are dog-specific traits
+dog_trait(is_long_elongated_skull).
+dog_trait(is_prominent_muzzle).
+dog_trait(is_floppy_or_rounded_ear).
+dog_trait(is_wide_spaced_ear).
+dog_trait(is_small_eye).
+dog_trait(is_forward_facing_eye).
+dog_trait(is_large_paw_with_non_retractable_claws).
+dog_trait(is_thick_tail).
+dog_trait(is_robust_upright_body).
+dog_trait(is_long_legs_relative_to_body).
+dog_trait(has_visible_stop_in_profile).
+dog_trait(has_overbite_or_underbite).
+
+% -------------------------------
+% SAMPLE QUERY
+% To test, you can run:
+% ?- is_cat.
+% ?- is_dog.
+% You may want to redefine facts like is_short_rounded_skull/0, etc., depending on image input.
+% -------------------------------
+```
+
+---
+
+### 🧠 How It Works
+
+- **Traits** are defined as simple predicates (e.g., `is_short_rounded_skull`, `is_large_round_eye`) that can be set to `true` or `false`.
+- The program counts how many traits match for cats and dogs.
+- If **three or more cat-specific traits** are present, and **no dog-specific traits**, it classifies as a **cat**.
+- Similarly, if three or more **dog-specific traits** are present, it classifies as a **dog**.
+
+---
+
+### 🧪 Example Queries
+
+```prolog
+?- is_cat.     % Returns true if enough cat traits are set to true
+?- is_dog.     % Returns true if enough dog traits are set to true
+```
+
+---
+
+### 🛠️ Extending for Image Input
+
+To use this with real image data (e.g., a computer vision pipeline), you would:
+
+- Set the facts dynamically from image analysis.
+- Use `assert/1` or `retract/1` to change trait values.
+- For example:
+```prolog
+?- assert(is_short_rounded_skull).
+?- is_cat.
+```
+
+Let me know if you want a version that integrates with actual image recognition libraries or uses probability-based reasoning!
--- a/.tmp-data/highlight_blurred.jpg
+++ b/.tmp-data/highlight_blurred.jpg
--- a/.tmp-data/highlight_border.jpg
+++ b/.tmp-data/highlight_border.jpg
--- a/.tmp-data/highlight_composite.jpg
+++ b/.tmp-data/highlight_composite.jpg
--- a/.tmp-data/highlight_desaturated.jpg
+++ b/.tmp-data/highlight_desaturated.jpg
--- a/.tmp-data/highlight_gradient_heatmap.jpg
+++ b/.tmp-data/highlight_gradient_heatmap.jpg
--- a/.tmp-data/highlight_overlay.jpg
+++ b/.tmp-data/highlight_overlay.jpg
--- a/.tmp-data/highlight_spotlight_heatmap.jpg
+++ b/.tmp-data/highlight_spotlight_heatmap.jpg
--- a/.tmp-data/prompts.md
+++ b/.tmp-data/prompts.md
--- a/main.py
+++ b/main.py
@@ -1,51 +0,0 @@
-import cv2
-
-from src.data import load_data
-from src.img_utils import (
-    apply_color_overlay,
-    apply_composite_overlay,
-    apply_gradient_heatmap_overlay,
-    apply_spotlight_heatmap,
-    blur_background,
-    desaturate_background,
-    draw_border,
-)
-
-
-def main():
-    print("Starting Abduction Demo")
-    labeled_images = load_data()
-    image = labeled_images[0].image
-    mask = labeled_images[0].create_mask([labeled_images[0].labels[0]])
-
-    overlay_result = apply_color_overlay(image, mask, color=(255, 100, 0), alpha=0.4)
-    cv2.imwrite(".tmp-data/highlight_overlay.jpg", overlay_result)
-
-    blur_result = blur_background(image, mask, blur_intensity=(51, 51))
-    cv2.imwrite(".tmp-data/highlight_blurred.jpg", blur_result)
-
-    desaturate_result = desaturate_background(image, mask)
-    cv2.imwrite(".tmp-data/highlight_desaturated.jpg", desaturate_result)
-
-    gradient_heatmap_result = apply_gradient_heatmap_overlay(image, mask, colormap=cv2.COLORMAP_JET, alpha=0.6)
-    cv2.imwrite(".tmp-data/highlight_gradient_heatmap.jpg", gradient_heatmap_result)
-
-    spotlight_result = apply_spotlight_heatmap(image, mask, darkness_factor=0.2)
-    cv2.imwrite(".tmp-data/highlight_spotlight_heatmap.jpg", spotlight_result)
-
-    composite_result = apply_composite_overlay(
-        image, mask, colormap=cv2.COLORMAP_JET, foreground_alpha=0.6, background_alpha=0.5
-    )
-    cv2.imwrite(".tmp-data/highlight_composite.jpg", composite_result)
-
-    bordered_image = draw_border(
-        image,
-        mask,
-        color=(50, 255, 50),  # A bright green color
-        thickness=8,
-    )
-    cv2.imwrite(".tmp-data/highlight_border.jpg", bordered_image)
-
-
-if __name__ == "__main__":
-    main()
--- a/main_basic.py
+++ b/main_basic.py
@@ -0,0 +1,97 @@
+from pathlib import Path
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_ollama.llms import OllamaLLM
+
+from src.data import LabeledImage, load_data
+from src.img_utils import encode_base64_resized
+
+TESTING = 1
+
+
+def reasoning():
+    template_reasoning = ChatPromptTemplate.from_messages([
+        ("system", "{role_reasoning}"),
+        ("human", "Question: {question_reasoning}"),
+    ])
+
+    model_reasoning = OllamaLLM(model="hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q4_K_M")
+    reasoning_chain = template_reasoning | model_reasoning | StrOutputParser()
+
+    description = reasoning_chain.invoke({
+        "role_reasoning": "You are an expert in the classification of whether an animal is a cat or a dog",
+        "question_reasoning": "I want you to do a comparative analysis of cats and dogs. Your analysis must use the inherent traits and biological characteristics of each species. You should list each of these characteristics so that an informed decision can be made about whether a given animal, e.g., in the form of an image, is a cat or a dog. Please provide a detailed analysis, focusing on traits and characteristics that can be extracted from a given image.",
+    })
+    return description
+
+
+def coding(description: str):
+    template_coding = ChatPromptTemplate.from_messages([
+        ("system", "{role_coding}"),
+        ("human", "Instructions: {instruction}\n Description: {description}"),
+    ])
+    model_coding = OllamaLLM(model="hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_XL")
+    coding_chain = template_coding | model_coding | StrOutputParser()
+
+    coding_description = coding_chain.invoke({
+        "role_coding": "You are an expert Prolog programmer with extended knowledge in reasoning and probabilities. Given instructions and a description, you can write a correct Prolog program that expresses the given task as a suitable logical program in Prolog",
+        "instruction": "Write a logical program for the following description",
+        "description": description,
+    })
+    return coding_description
+
+
+def grounding(coding_description: str, labeled_image: LabeledImage):
+    model_vl = OllamaLLM(model="qwen2.5vl:7b")
+    model_vl_ctx = model_vl.bind(
+        images=[encode_base64_resized(labeled_image.image_path, max_width=512, max_height=512, quality=70)]
+    )
+
+    template_grounding = ChatPromptTemplate.from_messages([
+        ("system", "{role_vl}"),
+        (
+            "human",
+            """Instructions: {instruction}
+
+            Description: {description}
+            """,
+        ),
+    ])
+    grounding_chain = template_grounding | model_vl_ctx | StrOutputParser()
+
+    return grounding_chain.invoke(
+        {
+            "role_vl": "You are an expert in analyzing an image to extract and match features of a given list.",
+            "instruction": """You are given a logic program in the following description and an image as input. Your task is to do the following steps:
+                1. Extract the list of features from the given Prolog program that contribute to deciding whether the image is a cat or a dog.
+                2. Match only the features present in the given image with the features you retrieved from the Prolog program. Give a likelihood of how sure you are with your matching. Print your result in this format:
+                    - <feature from prolog program>: <likelihood>
+                    - ...
+            """,
+            "description": coding_description,
+        },
+    )
+
+
+def main():
+    print("Starting Abduction Demo")
+    labeled_images = load_data()
+    labeled_image = labeled_images[1]
+    # image = labeled_image.image
+    # mask = labeled_image.create_mask([labeled_image.labels[0]])
+
+    if TESTING == 1:
+        coding_description = Path(".tmp-data/coding_description").open("r").read()
+    else:
+        coding_description = coding(reasoning())
+        with open(".tmp-data/coding_description", "w") as f:
+            f.write(coding_description)
+
+    result = grounding(coding_description, labeled_image)
+    # TODO: Feed this into the Prolog program and execute to reach final verdict
+    print(result)
+
+
+if __name__ == "__main__":
+    main()
--- a/main_xai.py
+++ b/main_xai.py
@@ -0,0 +1,101 @@
+from pathlib import Path
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_ollama.llms import OllamaLLM
+
+from src.data import LabeledImage, load_data
+from src.img_utils import encode_base64_resized
+
+TESTING = 1
+
+
+def reasoning():
+    template_reasoning = ChatPromptTemplate.from_messages([
+        ("system", "{role_reasoning}"),
+        ("human", "Question: {question_reasoning}"),
+    ])
+
+    model_reasoning = OllamaLLM(model="hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q4_K_M")
+    reasoning_chain = template_reasoning | model_reasoning | StrOutputParser()
+
+    description = reasoning_chain.invoke({
+        "role_reasoning": "You are an expert in the classification of whether an animal is a cat or a dog",
+        "question_reasoning": "I want you to do a comparative analysis of cats and dogs. Your analysis must use the inherent traits and biological characteristics of each species. You should list each of these characteristics so that an informed decision can be made about whether a given animal, e.g., in the form of an image, is a cat or a dog. Please provide a detailed analysis, focusing on traits and characteristics that can be extracted from a given image.",
+    })
+    return description
+
+
+def coding(description: str):
+    template_coding = ChatPromptTemplate.from_messages([
+        ("system", "{role_coding}"),
+        ("human", "Instructions: {instruction}\n Description: {description}"),
+    ])
+    model_coding = OllamaLLM(model="hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_XL")
+    coding_chain = template_coding | model_coding | StrOutputParser()
+
+    coding_description = coding_chain.invoke({
+        "role_coding": "You are an expert Prolog programmer with extended knowledge in reasoning and probabilities. Given instructions and a description, you can write a correct Prolog program that expresses the given task as a suitable logical program in Prolog",
+        "instruction": "Write a logical program for the following description",
+        "description": description,
+    })
+    return coding_description
+
+
+def grounding(coding_description: str, labeled_image: LabeledImage):
+    model_vl = OllamaLLM(model="qwen2.5vl:7b")
+    model_vl_ctx = model_vl.bind(
+        images=[
+            encode_base64_resized(
+                Path(".tmp-data/highlight_spotlight_heatmap.jpg"), max_width=512, max_height=512, quality=70
+            )
+        ]
+    )
+
+    template_grounding = ChatPromptTemplate.from_messages([
+        ("system", "{role_vl}"),
+        (
+            "human",
+            """Instructions: {instruction}
+
+            Description: {description}
+            """,
+        ),
+    ])
+    grounding_chain = template_grounding | model_vl_ctx | StrOutputParser()
+
+    return grounding_chain.invoke(
+        {
+            "role_vl": "You are an expert in analyzing an image to extract and match features of a given list.",
+            "instruction": """You are given a logic program in the following description and an image with a heatmap as input. Your task is to do the following steps:
+                1. Extract the list of features from the given Prolog program that contribute to deciding whether the image is a cat or a dog.
+                2. Match only the features highlighted by the heatmap in the given image with the features you retrieved from the Prolog program. Give a likelihood of how sure you are with your matching. Print your result in this format:
+                    - <feature from prolog program>: <likelihood>
+                    - ...
+            """,
+            "description": coding_description,
+        },
+    )
+
+
+def main():
+    print("Starting Abduction Demo")
+    labeled_images = load_data()
+    labeled_image = labeled_images[1]
+    # image = labeled_image.image
+    # mask = labeled_image.create_mask([labeled_image.labels[0]])
+
+    if TESTING == 1:
+        coding_description = Path(".tmp-data/coding_description").open("r").read()
+    else:
+        coding_description = coding(reasoning())
+        with open(".tmp-data/coding_description", "w") as f:
+            f.write(coding_description)
+
+    result = grounding(coding_description, labeled_image)
+    # TODO: Feed this into the Prolog program and execute to reach final verdict
+    print(result)
+
+
+if __name__ == "__main__":
+    main()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ dependencies = [
    "langchain-ollama>=0.3.6",
    "numpy>=2.3.2",
    "opencv-python>=4.11.0.86",
+    "langchain-community>=0.3.27",
 ]

 [tool.uv]
--- a/src/data.py
+++ b/src/data.py
@@ -5,6 +5,8 @@ from typing import List
 import cv2
 import numpy as np

+from src.img_utils import blur_background
+
 DATA_PATH_ROOT = Path("data").absolute()
 DATA_PATH_IMAGES = DATA_PATH_ROOT / "images"
 DATA_PATH_LABELS = DATA_PATH_ROOT / "labels"
@@ -58,3 +60,28 @@ def load_data():
    for img in DATA_PATH_IMAGES.glob("*.jpg"):
        limgs.append(LabeledImage(img, DATA_PATH_LABELS / (str(img.stem) + ".txt"), DATA_PATH_ROOT / "notes.json"))
    return limgs
+
+
+def get_feature_list(labeled_image: LabeledImage):
+    """Get a list of available features in the given image."""
+    return [entry["name"] for entry in labeled_image.labels]
+
+
+def emphasize_feature(labeled_image: LabeledImage, feature: str):
+    """Get the highlighted section with regard to the `feature` for the given image.
+    Used for further inspection of the given feature.
+
+    Args:
+        feature: name of the feature to inspect
+    """
+
+    categories = labeled_image.categories.values()
+    if feature in categories:
+        # get ids from feature
+        tmp = [(limg["id"] if limg["name"] == feature else None) for limg in labeled_image.labels]
+        ids = filter(None, tmp)
+        mask = labeled_image.create_mask([labeled_image.labels[x] for x in ids])
+        # return emphasized feature in image
+        return blur_background(labeled_image.image, mask, blur_intensity=(51, 51))
+    else:
+        return None
--- a/src/img_utils.py
+++ b/src/img_utils.py
@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import cv2
 import numpy as np

@@ -14,6 +16,10 @@ def apply_color_overlay(image, mask, color=(0, 0, 255), alpha=0.5):

    Returns:
        np.ndarray: The image with the colored overlay.
+
+    Examples:
+        overlay_result = apply_color_overlay(image, mask, color=(255, 100, 0), alpha=0.4)
+        cv2.imwrite("image.jpg", overlay_result)
    """
    # Create a colored layer
    overlay = np.zeros_like(image)
@@ -36,6 +42,10 @@ def desaturate_background(image, mask):

    Returns:
        np.ndarray: The image with a desaturated background.
+
+    Examples:
+        desaturate_result = desaturate_background(image, mask)
+        cv2.imwrite("image.jpg", desaturate_result)
    """
    # Create a grayscale version of the image, then convert it back to 3 channels
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -65,6 +75,10 @@ def blur_background(image, mask, blur_intensity=(35, 35)):

    Returns:
        np.ndarray: The image with a blurred background.
+
+    Examples:
+        blur_result = blur_background(image, mask, blur_intensity=(51, 51))
+        cv2.imwrite("image.jpg", blur_result)
    """
    # Create a blurred version of the image
    blurred_image = cv2.GaussianBlur(image, blur_intensity, 0)
@@ -94,6 +108,10 @@ def apply_gradient_heatmap_overlay(image, mask, colormap=cv2.COLORMAP_JET, alpha

    Returns:
        np.ndarray: The image with the gradient heatmap overlay.
+
+    Examples:
+        gradient_heatmap_result = apply_gradient_heatmap_overlay(image, mask, colormap=cv2.COLORMAP_JET, alpha=0.6)
+        cv2.imwrite("image.jpg", gradient_heatmap_result)
    """
    # 1. Create a distance transform from the mask.
    # This creates a float32 image where each pixel's value is its distance
@@ -130,6 +148,10 @@ def apply_spotlight_heatmap(image, mask, colormap=cv2.COLORMAP_JET, alpha=0.6, d

    Returns:
        np.ndarray: The image with the spotlight heatmap effect.
+
+    Examples:
+        spotlight_result = apply_spotlight_heatmap(image, mask, darkness_factor=0.2)
+        cv2.imwrite("image.jpg", spotlight_result)
    """
    # 1. Create the gradient heatmap region (same as the previous method)
    dist_transform = cv2.distanceTransform(mask, cv2.DIST_L2, 5)
@@ -168,6 +190,12 @@ def apply_composite_overlay(image, mask, colormap=cv2.COLORMAP_JET, foreground_a

    Returns:
        np.ndarray: The final composite image.
+
+    Examples:
+        composite_result = apply_composite_overlay(
+          image, mask, colormap=cv2.COLORMAP_JET, foreground_alpha=0.6, background_alpha=0.5
+        )
+        cv2.imwrite("image.jpg", composite_result)
    """
    # === Part 1: Create the Highlighted Foreground ===

@@ -220,6 +248,15 @@ def draw_border(image, mask, color=(0, 255, 0), thickness=3):

    Returns:
        np.ndarray: The image with the border drawn on it.
+
+    Examples:
+        bordered_image = draw_border(
+            image,
+            mask,
+            color=(50, 255, 50),  # A bright green color
+            thickness=8,
+        )
+        cv2.imwrite(".tmp-data/highlight_border.jpg", bordered_image)
    """
    # Create a copy to avoid modifying the original image
    output_image = image.copy()
@@ -233,3 +270,181 @@ def draw_border(image, mask, color=(0, 255, 0), thickness=3):
    cv2.drawContours(output_image, contours, -1, color, thickness)

    return output_image
+
+
+def encode_base64_simple(image_path: Path):
+    """
+    Simple base64 encoding using direct file reading (preserves original format).
+
+    Args:
+        image_path (Path): Path to the image file.
+
+    Returns:
+        str: Base64 encoded string of the image file.
+    """
+    import base64
+
+    try:
+        with open(image_path, "rb") as image_file:
+            image_base64 = base64.b64encode(image_file.read()).decode("utf-8")
+        return image_base64
+    except FileNotFoundError:
+        raise ValueError(f"Image file not found: {image_path}")
+    except Exception as e:
+        raise ValueError(f"Error reading image file: {e}")
+
+
+def encode_base64_resized(image_path: Path, max_width: int = 800, max_height: int = 600, quality: int = 85):
+    """
+    Resize image and encode to base64 to reduce size for LLM context.
+
+    Args:
+        image_path (Path): Path to the image file.
+        max_width (int): Maximum width for resized image.
+        max_height (int): Maximum height for resized image.
+        quality (int): JPEG compression quality (1-100).
+
+    Returns:
+        str: Base64 encoded string of the resized image.
+    """
+    import base64
+
+    try:
+        # Read the image
+        image = cv2.imread(str(image_path.absolute()))
+        if image is None:
+            raise ValueError(f"Could not read image: {image_path}")
+
+        # Get original dimensions
+        height, width = image.shape[:2]
+
+        # Calculate new dimensions while maintaining aspect ratio
+        if width > max_width or height > max_height:
+            # Calculate scaling factor
+            width_ratio = max_width / width
+            height_ratio = max_height / height
+            scale_factor = min(width_ratio, height_ratio)
+
+            new_width = int(width * scale_factor)
+            new_height = int(height * scale_factor)
+
+            # Resize the image
+            image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
+
+        # Encode as JPEG with specified quality
+        encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+        _, buffer = cv2.imencode(".jpg", image, encode_param)
+
+        # Convert to base64
+        image_base64 = base64.b64encode(buffer).decode("utf-8")
+        return image_base64
+
+    except FileNotFoundError:
+        raise ValueError(f"Image file not found: {image_path}")
+    except Exception as e:
+        raise ValueError(f"Error processing image: {e}")
+
+
+def encode_base64_aggressive_compression(
+    image_path: Path, max_width: int = 400, max_height: int = 400, quality: int = 50
+):
+    """
+    Aggressively compress image for minimal base64 size while preserving key features.
+
+    Args:
+        image_path (Path): Path to the image file.
+        max_width (int): Maximum width for resized image (smaller = less data).
+        max_height (int): Maximum height for resized image (smaller = less data).
+        quality (int): JPEG compression quality (1-100, lower = smaller file).
+
+    Returns:
+        str: Base64 encoded string of the heavily compressed image.
+    """
+    import base64
+
+    try:
+        # Read the image
+        image = cv2.imread(str(image_path))
+        if image is None:
+            raise ValueError(f"Could not read image: {image_path}")
+
+        # Get original dimensions
+        height, width = image.shape[:2]
+
+        # Calculate new dimensions with aggressive downsizing
+        width_ratio = max_width / width
+        height_ratio = max_height / height
+        scale_factor = min(width_ratio, height_ratio)
+
+        new_width = int(width * scale_factor)
+        new_height = int(height * scale_factor)
+
+        # Resize with high-quality interpolation for small images
+        image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
+
+        # Apply slight denoising to help compression
+        image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
+
+        # Encode as JPEG with aggressive compression
+        encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+        _, buffer = cv2.imencode(".jpg", image, encode_param)
+
+        # Convert to base64
+        image_base64 = base64.b64encode(buffer).decode("utf-8")
+
+        # Log the size for debugging
+        size_kb = len(image_base64) * 3 / 4 / 1024  # Approximate KB size
+        print(f"Compressed image to {new_width}x{new_height}, base64 size: ~{size_kb:.1f}KB")
+
+        return image_base64
+
+    except FileNotFoundError:
+        raise ValueError(f"Image file not found: {image_path}")
+    except Exception as e:
+        raise ValueError(f"Error processing image: {e}")
+
+
+def get_image_size_estimate(image_path: Path) -> dict:
+    """
+    Get size estimates for different compression levels without actually encoding.
+
+    Args:
+        image_path (Path): Path to the image file.
+
+    Returns:
+        dict: Size estimates for different compression settings.
+    """
+    import os
+
+    try:
+        # Get original file size
+        original_size_mb = os.path.getsize(image_path) / (1024 * 1024)
+
+        # Read image to get dimensions
+        image = cv2.imread(str(image_path))
+        if image is None:
+            raise ValueError(f"Could not read image: {image_path}")
+
+        height, width = image.shape[:2]
+
+        # Estimate base64 sizes for different settings
+        estimates = {
+            "original": {
+                "dimensions": f"{width}x{height}",
+                "file_size_mb": original_size_mb,
+                "estimated_base64_mb": original_size_mb * 1.33,  # Base64 overhead
+            },
+            "standard_compression": {
+                "dimensions": "800x600 (max)",
+                "estimated_base64_kb": 150,  # Rough estimate
+            },
+            "aggressive_compression": {
+                "dimensions": "400x400 (max)",
+                "estimated_base64_kb": 50,  # Rough estimate
+            },
+        }
+
+        return estimates
+
+    except Exception as e:
+        return {"error": str(e)}
--- a/uv.lock
+++ b/uv.lock
@@ -12,6 +12,7 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
    { name = "langchain", marker = "python_full_version >= '3.13'" },
+    { name = "langchain-community", marker = "python_full_version >= '3.13'" },
    { name = "langchain-ollama", marker = "python_full_version >= '3.13'" },
    { name = "numpy", marker = "python_full_version >= '3.13'" },
    { name = "opencv-python", marker = "python_full_version >= '3.13'" },
@@ -28,6 +29,7 @@ dev = [
 [package.metadata]
 requires-dist = [
    { name = "langchain", specifier = ">=0.3.27" },
+    { name = "langchain-community", specifier = ">=0.3.27" },
    { name = "langchain-ollama", specifier = ">=0.3.6" },
    { name = "numpy", specifier = ">=2.3.2" },
    { name = "opencv-python", specifier = ">=4.11.0.86" },
@@ -41,6 +43,61 @@ dev = [
    { name = "ty", specifier = ">=0.0.1a16" },
 ]

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+version = "3.12.15"
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+    { name = "multidict", marker = "python_full_version >= '3.13'" },
+    { name = "propcache", marker = "python_full_version >= '3.13'" },
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