main.py

import logging
import cv2
import numpy as np
import requests
import torch
import base64
import gradio as gr
from PIL import Image
from io import BytesIO
from fastapi import FastAPI
from models import TextSimilarityRequest
from extract_text import extract_text_from_image
from text_similarity import analyze_similarity
from app import generate_gradio


logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
app = FastAPI()

@app.post("/text_similarity", summary="Perform images text similarity", response_model=float, tags=["Text Similarities"])
async def text_similarity(request: TextSimilarityRequest):
    image_info = request.imageInfo
    key_texts = request.keyTexts
    similarity_threshold = request.similarityThreshold 
    origin_id = image_info.originId

    logging.info(f"Checking text similarity for main source with resource id {origin_id}")

    image = load_image_url(image_info.source)

    # Extract text from the image using the user's method
    gpu_available = torch.cuda.is_available()
    extracted_texts = extract_text_from_image(image, gpu_available)

    results = analyze_similarity(
            extracted_texts,
            key_texts,
            similarity_threshold=similarity_threshold/100,  # Convert percentage to decimal
            fragment_threshold=100/100  # Convert percentage to decimal
        )
    
    log_similarity_report(results, origin_id)
    
    total_texts = len(key_texts)
    passed_texts = results["statistics"]["direct_similarity"] + results["statistics"]["combined"]

    percentage_passed = (passed_texts / total_texts) * 100

    logging.info(f"Text similarity for main source with resource id {origin_id} is {percentage_passed}%")

    return percentage_passed

def log_similarity_report(results, originId):    
    # General statistics
    logging.info(f"[{originId}] Total texts analyzed: {results['statistics']['total_analyzed']}")
    logging.info(f"[{originId}] Texts with detected similarity: {results['statistics']['total_processed']}")
    
    # Similar texts
    if results["similar_texts"]:
        logging.info(f"[{originId}] Direct Similar Texts Found: {len(results['similar_texts'])}")
        for item in results["similar_texts"]:
            logging.info(f"[{originId}] Similar Text: '{item['text']}' -> Key Text: '{item['key_text']}' with Similarity: {item['similarity']:.2%}")
    
    # Detected fragments
    if results["fragments_detected"]:
        logging.info(f"[{originId}] Fragments Detected: {len(results['fragments_detected'])}")
        for item in results["fragments_detected"]:
            logging.info(f"[{originId}] Fragment: '{item['text']}' -> Key Text: '{item['key_text']}' with Similarity: {item['similarity']:.2%}")
    
    # Combined texts
    if results["combined"]:
        logging.info(f"[{originId}] Texts to be Combined: {len(results['combined'])}")
        for item in results["combined"]:
            logging.info(f"[{originId}] Combined Text: '{item['combined_text']}' -> Key Text: '{item['key_text']}' with Similarity: {item['similarity']:.2%}")
    
    # If no significant similarity found
    if not (results["similar_texts"] or results["fragments_detected"] or results["combined"]):
        logging.info(f"[{originId}] No significant similarity found.")
    
    # Statistics
    logging.info(f"[{originId}] Direct similarity: {results['statistics']['direct_similarity']}")
    logging.info(f"[{originId}] Fragments: {results['statistics']['fragments']}")
    logging.info(f"[{originId}] Combined: {results['statistics']['combined']}")

def load_image_url(source):
    Image.MAX_IMAGE_PIXELS = None

    if source.startswith('http'):
        response = requests.get(source)
        img = np.asarray(bytearray(response.content), dtype=np.uint8)
        img = cv2.imdecode(img, cv2.IMREAD_GRAYSCALE)
    else:
        img = base64.b64decode(source)
        img = Image.open(BytesIO(img))
        img = np.array(img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    return img

@app.on_event("startup")
async def startup_event():
    gr.mount_gradio_app(app, generate_gradio(), path="/")