from attachments import Attachments
from attachments.data import get_sample_path

# Option 1: Use included sample files (works offline)
pdf_path = get_sample_path("sample.pdf")
txt_path = get_sample_path("sample.txt")
ctx = Attachments(pdf_path, txt_path)

print(str(ctx))      # Pretty text view
print(len(ctx.images))  # Number of extracted images

# Try different file types
docx_path = get_sample_path("test_document.docx")
csv_path = get_sample_path("test.csv")
json_path = get_sample_path("sample.json")

ctx = Attachments(docx_path, csv_path, json_path)
print(f"Processed {len(ctx)} files: Word doc, CSV data, and JSON")

# Option 2: Use URLs (same API, works with any URL)
ctx = Attachments(
    "https://github.com/MaximeRivest/attachments/raw/main/src/attachments/data/sample.pdf",
    "https://github.com/MaximeRivest/attachments/raw/main/src/attachments/data/sample_multipage.pptx"
)

print(str(ctx))      # Pretty text view  
print(len(ctx.images))  # Number of extracted images

from attachments import Attachments

a = Attachments(
    "https://github.com/MaximeRivest/attachments/raw/main/src/attachments/data/" \
    "sample_multipage.pptx[3-5]"
)
print(a)           # pretty text view
len(a.images)      # 👉 base64 PNG list

from openai import OpenAI
from attachments import Attachments

pptx = Attachments("https://github.com/MaximeRivest/attachments/raw/main/src/attachments/data/sample_multipage.pptx[3-5]")

client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4.1-nano",
    messages=pptx.openai_chat("Analyse the following document:")
)
print(resp.choices[0].message.content)

from openai import OpenAI
from attachments import Attachments

pptx = Attachments("https://github.com/MaximeRivest/attachments/raw/main/src/attachments/data/sample_multipage.pptx[3-5]")

client = OpenAI()
resp = client.responses.create(
    input=pptx.openai_responses("Analyse the following document:"),
    model="gpt-4.1-nano"
)
print(resp.output[0].content[0].text)

import anthropic
from attachments import Attachments

pptx = Attachments("https://github.com/MaximeRivest/attachments/raw/main/src/attachments/data/sample_multipage.pptx[3-5]")

msg = anthropic.Anthropic().messages.create(
    model="claude-3-5-haiku-20241022",
    max_tokens=8_192,
    messages=pptx.claude("Analyse the slides:")
)
print(msg.content)

from attachments.dspy import Attachments  # Automatic type registration!
import dspy

# Configure DSPy
dspy.configure(lm=dspy.LM('openai/gpt-4.1-nano'))

# Both approaches work seamlessly:

# 1. Class-based signatures (recommended)
class DocumentAnalyzer(dspy.Signature):
    """Analyze document content and extract insights."""
    document: Attachments = dspy.InputField()
    insights: str = dspy.OutputField()

# 2. String-based signatures (works automatically!)
analyzer = dspy.Signature("document: Attachments -> insights: str")

# Use with any file type
doc = Attachments("report.pdf")
result = dspy.ChainOfThought(DocumentAnalyzer)(document=doc)
print(result.insights)

# CSS selector highlighting examples
title = Attachments("https://example.com[select:h1]")  # Highlights H1 elements
content = Attachments("https://example.com[select:.content]")  # Highlights .content class
main = Attachments("https://example.com[select:#main]")  # Highlights #main ID

# Multiple elements with counters and different colors
multi = Attachments("https://example.com[select:h1, .important][viewport:1920x1080]")

# Office format examples
presentation = Attachments("slides.pptx[1-5]")  # Extract specific slides
document = Attachments("report.docx")           # Word document processing
spreadsheet = Attachments("data.xlsx[summary:true]")  # Excel with summary

from attachments import attach, load, modify, present, refine, adapt

# Custom processing pipeline
result = (attach("document.pdf[pages:1-5]") 
         | load.pdf_to_pdfplumber 
         | modify.pages 
         | present.markdown + present.images
         | refine.add_headers | refine.truncate
         | adapt.claude("Analyze this content"))

# Web scraping pipeline
title = (attach("https://en.wikipedia.org/wiki/Llama[select:title]")
        | load.url_to_bs4 
        | modify.select 
        | present.text)

# Reusable processors
csv_analyzer = (load.csv_to_pandas 
               | modify.limit 
               | present.head + present.summary + present.metadata
               | refine.add_headers)

# Use as function
result = csv_analyzer("data.csv[limit:1000]")
analysis = result.claude("What patterns do you see?")

# PDF with image tiling and analysis
result = Attachments("report.pdf[tile:2x3][resize_images:400]")
analysis = result.claude("Analyze both text and visual elements")

# Multiple file types in one context
ctx = Attachments("report.pdf", "data.csv", "chart.png")
comparison = ctx.openai("Compare insights across all documents")

# Codebase structure only
structure = Attachments("./my-project[mode:structure]")

# Full codebase analysis with smart filtering
codebase = Attachments("./my-project[ignore:standard]")
review = codebase.claude("Review this code for best practices")

# Custom ignore patterns
filtered = Attachments("./app[ignore:.env,*.log,node_modules]")

# Extract specific content from web pages
title = Attachments("https://example.com[select:h1]")
paragraphs = Attachments("https://example.com[select:p]")

# Visual highlighting in screenshots with animations
highlighted = Attachments("https://example.com[select:h1][viewport:1920x1080]")
# Creates screenshot with animated highlighting of h1 elements

# Multiple element highlighting with counters
multi_select = Attachments("https://example.com[select:h1, .important][fullpage:true]")
# Shows "H1 (1/3)", "DIV (2/3)", etc. with different colors for multiple selections

# Pipeline approach for complex scraping
content = (attach("https://en.wikipedia.org/wiki/Llama[select:p]")
          | load.url_to_bs4 
          | modify.select 
          | present.text
          | refine.truncate)

# HEIC support with transformations
processed = Attachments("IMG_2160.HEIC[crop:100,100,400,300][rotate:90]")

# Batch image processing with tiling
collage = Attachments("photos.zip[tile:3x2][resize_images:800]")
description = collage.claude("Describe this image collage")

# Rich data presentation
data_summary = Attachments("sales_data.csv[limit:1000][summary:true]")

# Pipeline for complex data processing
result = (attach("data.csv[limit:500]")
         | load.csv_to_pandas 
         | modify.limit
         | present.head + present.summary + present.metadata
         | refine.add_headers
         | adapt.claude("What trends do you see?"))

# my_ocr_presenter.py
from attachments.core import Attachment, presenter

@presenter
def ocr_text(att: Attachment, pil_image: 'PIL.Image.Image') -> Attachment:
    """Extract text from images using OCR."""
    try:
        import pytesseract
        
        # Extract text using OCR
        extracted_text = pytesseract.image_to_string(pil_image)
        
        # Add OCR text to attachment
        att.text += f"\n## OCR Extracted Text\n\n{extracted_text}\n"
        
        # Add metadata
        att.metadata['ocr_extracted'] = True
        att.metadata['ocr_text_length'] = len(extracted_text)
        
        return att
        
    except ImportError:
        att.text += "\n## OCR Not Available\n\nInstall pytesseract: pip install pytesseract\n"
        return att