import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
import spacy
from typing import List, Dict, Tuple
import logging
import os
import gradio as gr
from fastapi.middleware.cors import CORSMiddleware
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import time
from datetime import datetime
import openpyxl
from openpyxl import Workbook
from openpyxl.utils import get_column_letter
from io import BytesIO
import base64
import hashlib
import requests
import tempfile
from pathlib import Path
import mimetypes
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Constants
MAX_LENGTH = 512
MODEL_NAME = "microsoft/deberta-v3-small"
WINDOW_SIZE = 6
WINDOW_OVERLAP = 2
CONFIDENCE_THRESHOLD = 0.65
BATCH_SIZE = 8 # Reduced batch size for CPU
MAX_WORKERS = 4 # Number of worker threads for processing
# IMPORTANT: Set PyTorch thread configuration at the module level
# before any parallel work starts
if not torch.cuda.is_available():
# Set thread configuration only once at the beginning
torch.set_num_threads(MAX_WORKERS)
try:
# Only set interop threads if it hasn't been set already
torch.set_num_interop_threads(MAX_WORKERS)
except RuntimeError as e:
logger.warning(f"Could not set interop threads: {str(e)}")
# Get password hash from environment variable (more secure)
ADMIN_PASSWORD_HASH = os.environ.get('ADMIN_PASSWORD_HASH')
if not ADMIN_PASSWORD_HASH:
ADMIN_PASSWORD_HASH = "5e22d1ed71b273b1b2b5331f2d3e0f6cf34595236f201c6924d6bc81de27cdcb"
# Excel file path for logs
EXCEL_LOG_PATH = "/tmp/prediction_logs.xlsx"
# OCR API settings
OCR_API_KEY = "9e11346f1288957" # This is a partial key - replace with the full one
OCR_API_ENDPOINT = "https://api.ocr.space/parse/image"
OCR_MAX_PDF_PAGES = 3
OCR_MAX_FILE_SIZE_MB = 1
# Configure logging for OCR module
ocr_logger = logging.getLogger("ocr_module")
ocr_logger.setLevel(logging.INFO)
class OCRProcessor:
"""
Handles OCR processing of image and document files using OCR.space API
"""
def __init__(self, api_key: str = OCR_API_KEY):
self.api_key = api_key
self.endpoint = OCR_API_ENDPOINT
def process_file(self, file_path: str) -> Dict:
"""
Process a file using OCR.space API
"""
start_time = time.time()
ocr_logger.info(f"Starting OCR processing for file: {os.path.basename(file_path)}")
# Validate file size
file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
if file_size_mb > OCR_MAX_FILE_SIZE_MB:
ocr_logger.warning(f"File size ({file_size_mb:.2f} MB) exceeds limit of {OCR_MAX_FILE_SIZE_MB} MB")
return {
"success": False,
"error": f"File size ({file_size_mb:.2f} MB) exceeds limit of {OCR_MAX_FILE_SIZE_MB} MB",
"text": ""
}
# Determine file type and handle accordingly
file_type = self._get_file_type(file_path)
ocr_logger.info(f"Detected file type: {file_type}")
# Prepare the API request
with open(file_path, 'rb') as f:
file_data = f.read()
# Set up API parameters
payload = {
'isOverlayRequired': 'false',
'language': 'eng',
'OCREngine': '2', # Use more accurate engine
'scale': 'true',
'detectOrientation': 'true',
}
# For PDF files, check page count limitations
if file_type == 'application/pdf':
ocr_logger.info("PDF document detected, enforcing page limit")
payload['filetype'] = 'PDF'
# Prepare file for OCR API
files = {
'file': (os.path.basename(file_path), file_data, file_type)
}
headers = {
'apikey': self.api_key,
}
# Make the OCR API request
try:
ocr_logger.info("Sending request to OCR.space API")
response = requests.post(
self.endpoint,
files=files,
data=payload,
headers=headers
)
response.raise_for_status()
result = response.json()
# Process the OCR results
if result.get('OCRExitCode') in [1, 2]: # Success or partial success
extracted_text = self._extract_text_from_result(result)
processing_time = time.time() - start_time
ocr_logger.info(f"OCR processing completed in {processing_time:.2f} seconds")
return {
"success": True,
"text": extracted_text,
"word_count": len(extracted_text.split()),
"processing_time_ms": int(processing_time * 1000)
}
else:
ocr_logger.error(f"OCR API error: {result.get('ErrorMessage', 'Unknown error')}")
return {
"success": False,
"error": result.get('ErrorMessage', 'OCR processing failed'),
"text": ""
}
except requests.exceptions.RequestException as e:
ocr_logger.error(f"OCR API request failed: {str(e)}")
return {
"success": False,
"error": f"OCR API request failed: {str(e)}",
"text": ""
}
def _extract_text_from_result(self, result: Dict) -> str:
"""
Extract all text from the OCR API result
"""
extracted_text = ""
if 'ParsedResults' in result and result['ParsedResults']:
for parsed_result in result['ParsedResults']:
if parsed_result.get('ParsedText'):
extracted_text += parsed_result['ParsedText']
return extracted_text
def _get_file_type(self, file_path: str) -> str:
"""
Determine MIME type of a file
"""
mime_type, _ = mimetypes.guess_type(file_path)
if mime_type is None:
# Default to binary if MIME type can't be determined
return 'application/octet-stream'
return mime_type
def is_admin_password(input_text: str) -> bool:
"""
Check if the input text matches the admin password using secure hash comparison.
"""
# Hash the input text
input_hash = hashlib.sha256(input_text.strip().encode()).hexdigest()
# Compare hashes (constant-time comparison to prevent timing attacks)
return input_hash == ADMIN_PASSWORD_HASH
class TextWindowProcessor:
def __init__(self):
try:
self.nlp = spacy.load("en_core_web_sm")
except OSError:
logger.info("Downloading spacy model...")
spacy.cli.download("en_core_web_sm")
self.nlp = spacy.load("en_core_web_sm")
if 'sentencizer' not in self.nlp.pipe_names:
self.nlp.add_pipe('sentencizer')
disabled_pipes = [pipe for pipe in self.nlp.pipe_names if pipe != 'sentencizer']
self.nlp.disable_pipes(*disabled_pipes)
# Initialize thread pool for parallel processing
self.executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
def split_into_sentences(self, text: str) -> List[str]:
doc = self.nlp(text)
return [str(sent).strip() for sent in doc.sents]
def create_windows(self, sentences: List[str], window_size: int, overlap: int) -> List[str]:
if len(sentences) < window_size:
return [" ".join(sentences)]
windows = []
stride = window_size - overlap
for i in range(0, len(sentences) - window_size + 1, stride):
window = sentences[i:i + window_size]
windows.append(" ".join(window))
return windows
def create_centered_windows(self, sentences: List[str], window_size: int) -> Tuple[List[str], List[List[int]]]:
"""Create windows with better boundary handling"""
windows = []
window_sentence_indices = []
for i in range(len(sentences)):
# Calculate window boundaries centered on current sentence
half_window = window_size // 2
start_idx = max(0, i - half_window)
end_idx = min(len(sentences), i + half_window + 1)
# Create the window
window = sentences[start_idx:end_idx]
windows.append(" ".join(window))
window_sentence_indices.append(list(range(start_idx, end_idx)))
return windows, window_sentence_indices
class TextClassifier:
def __init__(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model_name = MODEL_NAME
self.tokenizer = None
self.model = None
self.processor = TextWindowProcessor()
self.initialize_model()
def initialize_model(self):
"""Initialize the model and tokenizer."""
logger.info("Initializing model and tokenizer...")
from transformers import DebertaV2TokenizerFast
self.tokenizer = DebertaV2TokenizerFast.from_pretrained(
self.model_name,
model_max_length=MAX_LENGTH,
use_fast=True
)
self.model = AutoModelForSequenceClassification.from_pretrained(
self.model_name,
num_labels=2
).to(self.device)
model_path = "model_20250209_184929_acc1.0000.pt"
if os.path.exists(model_path):
logger.info(f"Loading custom model from {model_path}")
checkpoint = torch.load(model_path, map_location=self.device)
self.model.load_state_dict(checkpoint['model_state_dict'])
else:
logger.warning("Custom model file not found. Using base model.")
self.model.eval()
# [Other TextClassifier methods remain the same as in paste.txt]
def quick_scan(self, text: str) -> Dict:
"""Perform a quick scan using simple window analysis."""
if not text.strip():
return {
'prediction': 'unknown',
'confidence': 0.0,
'num_windows': 0
}
sentences = self.processor.split_into_sentences(text)
windows = self.processor.create_windows(sentences, WINDOW_SIZE, WINDOW_OVERLAP)
predictions = []
# Process windows in smaller batches for CPU efficiency
for i in range(0, len(windows), BATCH_SIZE):
batch_windows = windows[i:i + BATCH_SIZE]
inputs = self.tokenizer(
batch_windows,
truncation=True,
padding=True,
max_length=MAX_LENGTH,
return_tensors="pt"
).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
probs = F.softmax(outputs.logits, dim=-1)
for idx, window in enumerate(batch_windows):
prediction = {
'window': window,
'human_prob': probs[idx][1].item(),
'ai_prob': probs[idx][0].item(),
'prediction': 'human' if probs[idx][1] > probs[idx][0] else 'ai'
}
predictions.append(prediction)
# Clean up GPU memory if available
del inputs, outputs, probs
if torch.cuda.is_available():
torch.cuda.empty_cache()
if not predictions:
return {
'prediction': 'unknown',
'confidence': 0.0,
'num_windows': 0
}
avg_human_prob = sum(p['human_prob'] for p in predictions) / len(predictions)
avg_ai_prob = sum(p['ai_prob'] for p in predictions) / len(predictions)
return {
'prediction': 'human' if avg_human_prob > avg_ai_prob else 'ai',
'confidence': max(avg_human_prob, avg_ai_prob),
'num_windows': len(predictions)
}
def detailed_scan(self, text: str) -> Dict:
"""Perform a detailed scan with improved sentence-level analysis."""
# Clean up trailing whitespace
text = text.rstrip()
if not text.strip():
return {
'sentence_predictions': [],
'highlighted_text': '',
'full_text': '',
'overall_prediction': {
'prediction': 'unknown',
'confidence': 0.0,
'num_sentences': 0
}
}
sentences = self.processor.split_into_sentences(text)
if not sentences:
return {}
# Create centered windows for each sentence
windows, window_sentence_indices = self.processor.create_centered_windows(sentences, WINDOW_SIZE)
# Track scores for each sentence
sentence_appearances = {i: 0 for i in range(len(sentences))}
sentence_scores = {i: {'human_prob': 0.0, 'ai_prob': 0.0} for i in range(len(sentences))}
# Process windows in batches
for i in range(0, len(windows), BATCH_SIZE):
batch_windows = windows[i:i + BATCH_SIZE]
batch_indices = window_sentence_indices[i:i + BATCH_SIZE]
inputs = self.tokenizer(
batch_windows,
truncation=True,
padding=True,
max_length=MAX_LENGTH,
return_tensors="pt"
).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
probs = F.softmax(outputs.logits, dim=-1)
# Attribute predictions with weighted scoring
for window_idx, indices in enumerate(batch_indices):
center_idx = len(indices) // 2
center_weight = 0.7 # Higher weight for center sentence
edge_weight = 0.3 / (len(indices) - 1) if len(indices) > 1 else 0 # Distribute remaining weight
for pos, sent_idx in enumerate(indices):
# Apply higher weight to center sentence
weight = center_weight if pos == center_idx else edge_weight
sentence_appearances[sent_idx] += weight
sentence_scores[sent_idx]['human_prob'] += weight * probs[window_idx][1].item()
sentence_scores[sent_idx]['ai_prob'] += weight * probs[window_idx][0].item()
# Clean up memory
del inputs, outputs, probs
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Calculate final predictions with boundary smoothing
sentence_predictions = []
for i in range(len(sentences)):
if sentence_appearances[i] > 0:
human_prob = sentence_scores[i]['human_prob'] / sentence_appearances[i]
ai_prob = sentence_scores[i]['ai_prob'] / sentence_appearances[i]
# Apply minimal smoothing at prediction boundaries
if i > 0 and i < len(sentences) - 1:
prev_human = sentence_scores[i-1]['human_prob'] / max(sentence_appearances[i-1], 1e-10)
prev_ai = sentence_scores[i-1]['ai_prob'] / max(sentence_appearances[i-1], 1e-10)
next_human = sentence_scores[i+1]['human_prob'] / max(sentence_appearances[i+1], 1e-10)
next_ai = sentence_scores[i+1]['ai_prob'] / max(sentence_appearances[i+1], 1e-10)
# Check if we're at a prediction boundary
current_pred = 'human' if human_prob > ai_prob else 'ai'
prev_pred = 'human' if prev_human > prev_ai else 'ai'
next_pred = 'human' if next_human > next_ai else 'ai'
if current_pred != prev_pred or current_pred != next_pred:
# Small adjustment at boundaries
smooth_factor = 0.1
human_prob = (human_prob * (1 - smooth_factor) +
(prev_human + next_human) * smooth_factor / 2)
ai_prob = (ai_prob * (1 - smooth_factor) +
(prev_ai + next_ai) * smooth_factor / 2)
sentence_predictions.append({
'sentence': sentences[i],
'human_prob': human_prob,
'ai_prob': ai_prob,
'prediction': 'human' if human_prob > ai_prob else 'ai',
'confidence': max(human_prob, ai_prob)
})
return {
'sentence_predictions': sentence_predictions,
'highlighted_text': self.format_predictions_html(sentence_predictions),
'full_text': text,
'overall_prediction': self.aggregate_predictions(sentence_predictions)
}
def format_predictions_html(self, sentence_predictions: List[Dict]) -> str:
"""Format predictions as HTML with color-coding."""
html_parts = []
for pred in sentence_predictions:
sentence = pred['sentence']
confidence = pred['confidence']
if confidence >= CONFIDENCE_THRESHOLD:
if pred['prediction'] == 'human':
color = "#90EE90" # Light green
else:
color = "#FFB6C6" # Light red
else:
if pred['prediction'] == 'human':
color = "#E8F5E9" # Very light green
else:
color = "#FFEBEE" # Very light red
html_parts.append(f'{sentence}')
return " ".join(html_parts)
def aggregate_predictions(self, predictions: List[Dict]) -> Dict:
"""Aggregate predictions from multiple sentences into a single prediction."""
if not predictions:
return {
'prediction': 'unknown',
'confidence': 0.0,
'num_sentences': 0
}
total_human_prob = sum(p['human_prob'] for p in predictions)
total_ai_prob = sum(p['ai_prob'] for p in predictions)
num_sentences = len(predictions)
avg_human_prob = total_human_prob / num_sentences
avg_ai_prob = total_ai_prob / num_sentences
return {
'prediction': 'human' if avg_human_prob > avg_ai_prob else 'ai',
'confidence': max(avg_human_prob, avg_ai_prob),
'num_sentences': num_sentences
}
# Function to handle file upload, OCR processing, and text analysis
def handle_file_upload_and_analyze(file_obj, mode: str, classifier) -> tuple:
"""
Handle file upload, OCR processing, and text analysis
"""
if file_obj is None:
return (
"No file uploaded",
"Please upload a file to analyze",
"No file uploaded for analysis"
)
# Create a temporary file with an appropriate extension based on content
content_start = file_obj[:20] # Look at the first few bytes
# Default to .bin extension
file_ext = ".bin"
# Try to detect PDF files
if content_start.startswith(b'%PDF'):
file_ext = ".pdf"
# For images, detect by common magic numbers
elif content_start.startswith(b'\xff\xd8'): # JPEG
file_ext = ".jpg"
elif content_start.startswith(b'\x89PNG'): # PNG
file_ext = ".png"
elif content_start.startswith(b'GIF'): # GIF
file_ext = ".gif"
# Create a temporary file with the detected extension
with tempfile.NamedTemporaryFile(delete=False, suffix=file_ext) as temp_file:
temp_file_path = temp_file.name
# Write uploaded file data to the temporary file
temp_file.write(file_obj)
try:
# Process the file with OCR
ocr_processor = OCRProcessor()
ocr_result = ocr_processor.process_file(temp_file_path)
if not ocr_result["success"]:
return (
"OCR Processing Error",
ocr_result["error"],
"Failed to extract text from the uploaded file"
)
# Get the extracted text
extracted_text = ocr_result["text"]
# If no text was extracted
if not extracted_text.strip():
return (
"No text extracted",
"The OCR process did not extract any text from the uploaded file.",
"No text was found in the uploaded file"
)
# Call the original text analysis function with the extracted text
return analyze_text(extracted_text, mode, classifier)
finally:
# Clean up the temporary file
if os.path.exists(temp_file_path):
os.remove(temp_file_path)
def initialize_excel_log():
"""Initialize the Excel log file if it doesn't exist."""
if not os.path.exists(EXCEL_LOG_PATH):
wb = Workbook()
ws = wb.active
ws.title = "Prediction Logs"
# Set column headers
headers = ["timestamp", "word_count", "prediction", "confidence",
"execution_time_ms", "analysis_mode", "full_text"]
for col_num, header in enumerate(headers, 1):
ws.cell(row=1, column=col_num, value=header)
# Adjust column widths for better readability
ws.column_dimensions[get_column_letter(1)].width = 20 # timestamp
ws.column_dimensions[get_column_letter(2)].width = 10 # word_count
ws.column_dimensions[get_column_letter(3)].width = 10 # prediction
ws.column_dimensions[get_column_letter(4)].width = 10 # confidence
ws.column_dimensions[get_column_letter(5)].width = 15 # execution_time_ms
ws.column_dimensions[get_column_letter(6)].width = 15 # analysis_mode
ws.column_dimensions[get_column_letter(7)].width = 100 # full_text
# Save the workbook
wb.save(EXCEL_LOG_PATH)
logger.info(f"Initialized Excel log file at {EXCEL_LOG_PATH}")
def log_prediction_data(input_text, word_count, prediction, confidence, execution_time, mode):
"""Log prediction data to an Excel file in the /tmp directory."""
# Initialize the Excel file if it doesn't exist
if not os.path.exists(EXCEL_LOG_PATH):
initialize_excel_log()
try:
# Load the existing workbook
wb = openpyxl.load_workbook(EXCEL_LOG_PATH)
ws = wb.active
# Get the next row number
next_row = ws.max_row + 1
# Clean up the input text for Excel storage (replace problematic characters)
cleaned_text = input_text.replace("\n", " ")
# Prepare row data
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
row_data = [
timestamp,
word_count,
prediction,
f"{confidence:.2f}",
f"{execution_time:.2f}",
mode,
cleaned_text
]
# Add the data to the worksheet
for col_num, value in enumerate(row_data, 1):
ws.cell(row=next_row, column=col_num, value=value)
# Save the workbook
wb.save(EXCEL_LOG_PATH)
logger.info(f"Successfully logged prediction data to {EXCEL_LOG_PATH}")
return True
except Exception as e:
logger.error(f"Error logging prediction data to Excel: {str(e)}")
return False
def get_logs_as_base64():
"""Read the Excel logs file and return as base64 for downloading."""
if not os.path.exists(EXCEL_LOG_PATH):
return None
try:
# Read the Excel file into memory
with open(EXCEL_LOG_PATH, "rb") as f:
file_data = f.read()
# Encode the file as base64
base64_data = base64.b64encode(file_data).decode('utf-8')
return base64_data
except Exception as e:
logger.error(f"Error reading Excel logs: {str(e)}")
return None
def analyze_text(text: str, mode: str, classifier: TextClassifier) -> tuple:
"""Analyze text using specified mode and return formatted results."""
# Check if the input text matches the admin password using secure comparison
if is_admin_password(text.strip()):
# Return logs instead of analysis
base64_data = get_logs_as_base64()
logs_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
logs_filename = f"prediction_logs_{logs_timestamp}.xlsx"
if base64_data:
# Create downloadable HTML with the logs
html_content = f"""
No prediction logs were found or there was an error reading the logs file.
"""
# Return special admin output instead of normal analysis
return (
html_content,
f"Admin access granted. Logs retrieved at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
f"ADMIN MODE\nLogs available for download\nFile: {EXCEL_LOG_PATH}"
)
# Start timing for normal analysis
start_time = time.time()
# Count words in the text
word_count = len(text.split())
# If text is less than 200 words and detailed mode is selected, switch to quick mode
original_mode = mode
if word_count < 200 and mode == "detailed":
mode = "quick"
if mode == "quick":
result = classifier.quick_scan(text)
quick_analysis = f"""
PREDICTION: {result['prediction'].upper()}
Confidence: {result['confidence']*100:.1f}%
Windows analyzed: {result['num_windows']}
"""
# Add note if mode was switched
if original_mode == "detailed":
quick_analysis += f"\n\nNote: Switched to quick mode because text contains only {word_count} words. Minimum 200 words required for detailed analysis."
# Calculate execution time in milliseconds
execution_time = (time.time() - start_time) * 1000
# Log the prediction data
log_prediction_data(
input_text=text,
word_count=word_count,
prediction=result['prediction'],
confidence=result['confidence'],
execution_time=execution_time,
mode=original_mode
)
return (
text, # No highlighting in quick mode
"Quick scan mode - no sentence-level analysis available",
quick_analysis
)
else:
analysis = classifier.detailed_scan(text)
detailed_analysis = []
for pred in analysis['sentence_predictions']:
confidence = pred['confidence'] * 100
detailed_analysis.append(f"Sentence: {pred['sentence']}")
detailed_analysis.append(f"Prediction: {pred['prediction'].upper()}")
detailed_analysis.append(f"Confidence: {confidence:.1f}%")
detailed_analysis.append("-" * 50)
final_pred = analysis['overall_prediction']
overall_result = f"""
FINAL PREDICTION: {final_pred['prediction'].upper()}
Overall confidence: {final_pred['confidence']*100:.1f}%
Number of sentences analyzed: {final_pred['num_sentences']}
"""
# Calculate execution time in milliseconds
execution_time = (time.time() - start_time) * 1000
# Log the prediction data
log_prediction_data(
input_text=text,
word_count=word_count,
prediction=final_pred['prediction'],
confidence=final_pred['confidence'],
execution_time=execution_time,
mode=original_mode
)
return (
analysis['highlighted_text'],
"\n".join(detailed_analysis),
overall_result
)
# Initialize the classifier globally
classifier = TextClassifier()
# Create Gradio interface with a small file upload button next to the radio buttons
# Modified approach - simplify by using custom HTML/CSS to achieve the exact layout
def setup_interface():
# Create analyzer functions that capture the classifier
def analyze_text_wrapper(text, mode):
return analyze_text(text, mode, classifier)
def handle_file_upload_wrapper(file_obj, mode):
if file_obj is None:
return analyze_text_wrapper("", mode)
return handle_file_upload_and_analyze(file_obj, mode, classifier)
def clear_inputs():
return "", None, None, None
with gr.Blocks(title="AI Text Detector") as demo:
gr.Markdown("# AI Text Detector")
with gr.Row():
# Left column - Input
with gr.Column():
text_input = gr.Textbox(
lines=8,
placeholder="Enter text to analyze...",
label="Input Text"
)
gr.Markdown("Analysis Mode")
gr.Markdown("Quick mode for faster analysis. Detailed mode for sentence-level analysis.",
elem_classes=["description-text"])
# Container for radio buttons
with gr.Row(elem_id="radio-container"):
mode_selection = gr.Radio(
choices=["quick", "detailed"],
value="quick",
label=""
)
# Add paperclip as an HTML element next to the radio buttons
gr.HTML("""
""", elem_id="paperclip-html")
# Hidden file upload
file_upload = gr.File(
file_types=["image", "pdf", "doc", "docx"],
type="binary",
visible=False,
elem_id="hidden-file-upload"
)
# Action buttons row
with gr.Row():
clear_btn = gr.Button("Clear", elem_id="clear-btn")
analyze_btn = gr.Button("Analyze Text", elem_id="analyze-btn")
# Right column - Results
with gr.Column():
output_html = gr.HTML(label="Highlighted Analysis")
output_sentences = gr.Textbox(label="Sentence-by-Sentence Analysis", lines=10)
output_result = gr.Textbox(label="Overall Result", lines=4)
# Connect the components
analyze_btn.click(
analyze_text_wrapper,
inputs=[text_input, mode_selection],
outputs=[output_html, output_sentences, output_result]
)
clear_btn.click(
clear_inputs,
inputs=None,
outputs=[text_input, output_html, output_sentences, output_result]
)
file_upload.change(
handle_file_upload_wrapper,
inputs=[file_upload, mode_selection],
outputs=[output_html, output_sentences, output_result]
)
# Extensive custom CSS and JavaScript to make everything work properly
gr.HTML("""
""")
return demo
# Setup the app with CORS middleware
def setup_app():
demo = setup_interface()
# Get the FastAPI app from Gradio
app = demo.app
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # For development
allow_credentials=True,
allow_methods=["GET", "POST", "OPTIONS"],
allow_headers=["*"],
)
return demo
# Initialize the application
if __name__ == "__main__":
demo = setup_app()
# Start the server
demo.queue()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True
)