import gradio as gr
import json
import markdown
import cv2
import numpy as np
from PIL import Image
from transformers import AutoProcessor, VisionEncoderDecoderModel, AutoModelForImageTextToText
import torch
try:
    from sentence_transformers import SentenceTransformer
    import numpy as np
    from sklearn.metrics.pairwise import cosine_similarity
    RAG_DEPENDENCIES_AVAILABLE = True
except ImportError as e:
    print(f"RAG dependencies not available: {e}")
    print("Please install: pip install sentence-transformers scikit-learn")
    RAG_DEPENDENCIES_AVAILABLE = False
    SentenceTransformer = None
import os
import tempfile
import uuid
import base64
import io
from utils.utils import *
from utils.markdown_utils import MarkdownConverter

# Math extension is optional for enhanced math rendering
MATH_EXTENSION_AVAILABLE = False
try:
    from mdx_math import MathExtension
    MATH_EXTENSION_AVAILABLE = True
except ImportError:
    pass


class DOLPHIN:
    def __init__(self, model_id_or_path):
        """Initialize the Hugging Face model optimized for powerful GPU"""
        self.processor = AutoProcessor.from_pretrained(model_id_or_path)
        self.model = VisionEncoderDecoderModel.from_pretrained(
            model_id_or_path,
            torch_dtype=torch.float16,
            device_map="auto" if torch.cuda.is_available() else None
        )
        self.model.eval()
        
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        if not torch.cuda.is_available():
            self.model = self.model.float()
        
        self.tokenizer = self.processor.tokenizer
        
    def chat(self, prompt, image):
        """Process an image or batch of images with the given prompt(s)"""
        is_batch = isinstance(image, list)
        
        if not is_batch:
            images = [image]
            prompts = [prompt]
        else:
            images = image
            prompts = prompt if isinstance(prompt, list) else [prompt] * len(images)
        
        batch_inputs = self.processor(images, return_tensors="pt", padding=True)
        batch_pixel_values = batch_inputs.pixel_values
        
        if torch.cuda.is_available():
            batch_pixel_values = batch_pixel_values.half().to(self.device)
        else:
            batch_pixel_values = batch_pixel_values.to(self.device)
        
        prompts = [f"<s>{p} <Answer/>" for p in prompts]
        batch_prompt_inputs = self.tokenizer(
            prompts,
            add_special_tokens=False,
            return_tensors="pt"
        )

        batch_prompt_ids = batch_prompt_inputs.input_ids.to(self.device)
        batch_attention_mask = batch_prompt_inputs.attention_mask.to(self.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                pixel_values=batch_pixel_values,
                decoder_input_ids=batch_prompt_ids,
                decoder_attention_mask=batch_attention_mask,
                min_length=1,
                max_length=2048,
                pad_token_id=self.tokenizer.pad_token_id,
                eos_token_id=self.tokenizer.eos_token_id,
                use_cache=True,
                bad_words_ids=[[self.tokenizer.unk_token_id]],
                return_dict_in_generate=True,
                do_sample=False,
                num_beams=1,
                repetition_penalty=1.1,
                temperature=1.0
            )
        
        sequences = self.tokenizer.batch_decode(outputs.sequences, skip_special_tokens=False)
        
        results = []
        for i, sequence in enumerate(sequences):
            cleaned = sequence.replace(prompts[i], "").replace("<pad>", "").replace("</s>", "").strip()
            results.append(cleaned)
            
        if not is_batch:
            return results[0]
        return results


class Gemma3nModel:
    def __init__(self, model_id="google/gemma-3n-E4B-it"):
        """Initialize the Gemma 3n model for text generation and image description"""
        self.model_id = model_id
        self.processor = AutoProcessor.from_pretrained(model_id)
        self.model = AutoModelForImageTextToText.from_pretrained(
            model_id,
            torch_dtype="auto",
            device_map="auto"
        )
        self.model.eval()
        print(f"✅ Gemma 3n loaded (Device: {self.model.device}, DType: {self.model.dtype})")
        
    def generate_alt_text(self, pil_image):
        """Generate alt text for an image using local Gemma 3n"""
        try:
            # Ensure image is in RGB mode
            if pil_image.mode != 'RGB':
                pil_image = pil_image.convert('RGB')
            
            # Create a detailed prompt for alt text generation
            prompt = """You are an accessibility expert creating alt text for images to help visually impaired users understand visual content. Analyze this image and provide a clear, concise description that captures the essential visual information.

Focus on:
- Main subject or content of the image
- Important details, text, or data shown
- Layout and structure if relevant (charts, diagrams, tables)
- Context that would help someone understand the image's purpose

Provide a descriptive alt text in 1-2 sentences that is informative but not overly verbose. Start directly with the description without saying "This image shows" or similar phrases."""
            
            # Prepare the message format
            message = {
                "role": "user",
                "content": [
                    {"type": "image", "image": pil_image},
                    {"type": "text", "text": prompt}
                ]
            }
            
            # Apply chat template and generate
            input_ids = self.processor.apply_chat_template(
                [message],
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                return_tensors="pt",
            )
            input_len = input_ids["input_ids"].shape[-1]
            
            input_ids = input_ids.to(self.model.device, dtype=self.model.dtype)
            outputs = self.model.generate(
                **input_ids,
                max_new_tokens=256,
                disable_compile=True,
                do_sample=False,
                temperature=0.1
            )
            
            text = self.processor.batch_decode(
                outputs[:, input_len:],
                skip_special_tokens=True,
                clean_up_tokenization_spaces=True
            )
            
            alt_text = text[0].strip()
            
            # Clean up the alt text
            alt_text = alt_text.replace('\n', ' ').replace('\r', ' ')
            # Remove common prefixes if they appear
            prefixes_to_remove = ["This image shows", "The image shows", "This shows", "The figure shows"]
            for prefix in prefixes_to_remove:
                if alt_text.startswith(prefix):
                    alt_text = alt_text[len(prefix):].strip()
                    break
            
            return alt_text if alt_text else "Image description unavailable"
            
        except Exception as e:
            print(f"❌ Error generating alt text: {e}")
            import traceback
            traceback.print_exc()
            return "Image description unavailable"
    
    def chat(self, prompt, history=None):
        """Chat functionality using Gemma 3n for text-only conversations"""
        try:
            # Create message format
            message = {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt}
                ]
            }
            
            # If history exists, include it
            conversation = history if history else []
            conversation.append(message)
            
            # Apply chat template and generate
            input_ids = self.processor.apply_chat_template(
                conversation,
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                return_tensors="pt",
            )
            input_len = input_ids["input_ids"].shape[-1]
            
            input_ids = input_ids.to(self.model.device, dtype=self.model.dtype)
            outputs = self.model.generate(
                **input_ids,
                max_new_tokens=1024,
                disable_compile=True,
                do_sample=True,
                temperature=0.7
            )
            
            text = self.processor.batch_decode(
                outputs[:, input_len:],
                skip_special_tokens=True,
                clean_up_tokenization_spaces=True
            )
            
            return text[0].strip()
            
        except Exception as e:
            print(f"❌ Error in chat: {e}")
            import traceback
            traceback.print_exc()
            return f"Error generating response: {str(e)}"


def convert_pdf_to_images_gradio(pdf_file):
    """Convert uploaded PDF file to list of PIL Images"""
    try:
        import pymupdf
        
        if isinstance(pdf_file, str):
            pdf_document = pymupdf.open(pdf_file)
        else:
            pdf_bytes = pdf_file.read()
            pdf_document = pymupdf.open(stream=pdf_bytes, filetype="pdf")
        
        images = []
        for page_num in range(len(pdf_document)):
            page = pdf_document[page_num]
            mat = pymupdf.Matrix(2.0, 2.0)
            pix = page.get_pixmap(matrix=mat)
            img_data = pix.tobytes("png")
            pil_image = Image.open(io.BytesIO(img_data)).convert("RGB")
            images.append(pil_image)
        
        pdf_document.close()
        return images
        
    except Exception as e:
        raise Exception(f"Error converting PDF: {str(e)}")


def process_pdf_document(pdf_file, model, progress=gr.Progress()):
    """Process uploaded PDF file page by page"""
    if pdf_file is None:
        return "No PDF file uploaded", ""
    
    try:
        progress(0.1, desc="Converting PDF to images...")
        images = convert_pdf_to_images_gradio(pdf_file)
        
        if not images:
            return "Failed to convert PDF to images", ""
        
        all_results = []
        
        for page_idx, pil_image in enumerate(images):
            progress((page_idx + 1) / len(images) * 0.8 + 0.1, 
                    desc=f"Processing page {page_idx + 1}/{len(images)}...")
            
            layout_output = model.chat("Parse the reading order of this document.", pil_image)
            
            padded_image, dims = prepare_image(pil_image)
            recognition_results = process_elements_optimized(
                layout_output, 
                padded_image, 
                dims, 
                model, 
                max_batch_size=4
            )
            
            try:
                markdown_converter = MarkdownConverter()
                markdown_content = markdown_converter.convert(recognition_results)
            except:
                markdown_content = generate_fallback_markdown(recognition_results)
            
            page_result = {
                "page_number": page_idx + 1,
                "markdown": markdown_content
            }
            all_results.append(page_result)
        
        progress(1.0, desc="Processing complete!")
        
        combined_markdown = "\n\n---\n\n".join([
            f"# Page {result['page_number']}\n\n{result['markdown']}" 
            for result in all_results
        ])
        
        return combined_markdown, "processing_complete"
        
    except Exception as e:
        error_msg = f"Error processing PDF: {str(e)}"
        return error_msg, "error"


def process_elements_optimized(layout_results, padded_image, dims, model, max_batch_size=4):
    """Optimized element processing for powerful GPU"""
    layout_results = parse_layout_string(layout_results)
    
    text_elements = []
    table_elements = []
    figure_results = []
    previous_box = None
    reading_order = 0
    
    for bbox, label in layout_results:
        try:
            x1, y1, x2, y2, orig_x1, orig_y1, orig_x2, orig_y2, previous_box = process_coordinates(
                bbox, padded_image, dims, previous_box
            )
            
            cropped = padded_image[y1:y2, x1:x2]
            if cropped.size > 0 and cropped.shape[0] > 3 and cropped.shape[1] > 3:
                if label == "fig":
                    pil_crop = Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB))
                    pil_crop = crop_margin(pil_crop)
                    
                    # Generate alt text for accessibility using local Gemma 3n
                    alt_text = gemma_model.generate_alt_text(pil_crop)
                    
                    buffered = io.BytesIO()
                    pil_crop.save(buffered, format="PNG")
                    img_base64 = base64.b64encode(buffered.getvalue()).decode()
                    data_uri = f"data:image/png;base64,{img_base64}"
                    
                    figure_results.append({
                        "label": label,
                        "text": f"![{alt_text}]({data_uri})\n\n*{alt_text}*",
                        "bbox": [orig_x1, orig_y1, orig_x2, orig_y2],
                        "reading_order": reading_order,
                        "alt_text": alt_text,
                    })
                else:
                    pil_crop = Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB))
                    element_info = {
                        "crop": pil_crop,
                        "label": label,
                        "bbox": [orig_x1, orig_y1, orig_x2, orig_y2],
                        "reading_order": reading_order,
                    }
                    
                    if label == "tab":
                        table_elements.append(element_info)
                    else:
                        text_elements.append(element_info)
                        
            reading_order += 1
            
        except Exception as e:
            print(f"Error processing element {label}: {str(e)}")
            continue
    
    recognition_results = figure_results.copy()
    
    if text_elements:
        text_results = process_element_batch_optimized(
            text_elements, model, "Read text in the image.", max_batch_size
        )
        recognition_results.extend(text_results)
    
    if table_elements:
        table_results = process_element_batch_optimized(
            table_elements, model, "Parse the table in the image.", max_batch_size
        )
        recognition_results.extend(table_results)
    
    recognition_results.sort(key=lambda x: x.get("reading_order", 0))
    return recognition_results


def process_element_batch_optimized(elements, model, prompt, max_batch_size=4):
    """Process elements in batches for powerful GPU"""
    results = []
    batch_size = min(len(elements), max_batch_size)
    
    for i in range(0, len(elements), batch_size):
        batch_elements = elements[i:i+batch_size]
        crops_list = [elem["crop"] for elem in batch_elements]
        prompts_list = [prompt] * len(crops_list)
        
        batch_results = model.chat(prompts_list, crops_list)
        
        for j, result in enumerate(batch_results):
            elem = batch_elements[j]
            results.append({
                "label": elem["label"],
                "bbox": elem["bbox"],
                "text": result.strip(),
                "reading_order": elem["reading_order"],
            })
            
        del crops_list, batch_elements
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
    
    return results


def generate_fallback_markdown(recognition_results):
    """Generate basic markdown if converter fails"""
    markdown_content = ""
    for element in recognition_results:
        if element["label"] == "tab":
            markdown_content += f"\n\n{element['text']}\n\n"
        elif element["label"] in ["para", "title", "sec", "sub_sec"]:
            markdown_content += f"{element['text']}\n\n"
        elif element["label"] == "fig":
            # Image should already have alt text from processing
            markdown_content += f"{element['text']}\n\n"
    return markdown_content


# Initialize models
model_path = "./hf_model"
if not os.path.exists(model_path):
    model_path = "ByteDance/DOLPHIN"

# Model paths and configuration
model_path = "./hf_model" if os.path.exists("./hf_model") else "ByteDance/DOLPHIN"
hf_token = os.getenv('HF_TOKEN')
gemma_model_id = "google/gemma-3n-E4B-it"

# Initialize models
print("Loading DOLPHIN model...")
dolphin_model = DOLPHIN(model_path)
print(f"✅ DOLPHIN model loaded (Device: {dolphin_model.device})")

print("Loading Gemma 3n model...")
gemma_model = Gemma3nModel(gemma_model_id)

model_status = "✅ Both models loaded successfully"

# Initialize embedding model
if RAG_DEPENDENCIES_AVAILABLE:
    try:
        print("Loading embedding model for RAG...")
        embedding_model = SentenceTransformer('all-MiniLM-L6-v2', device='cpu')
        print("✅ Embedding model loaded successfully (CPU)")
    except Exception as e:
        print(f"❌ Error loading embedding model: {e}")
        embedding_model = None
else:
    print("❌ RAG dependencies not available")
    embedding_model = None


# Global state for managing tabs
processed_markdown = ""
show_results_tab = False
document_chunks = []
document_embeddings = None


def chunk_document(text, chunk_size=1024, overlap=100):
    """Split document into overlapping chunks for RAG"""
    words = text.split()
    chunks = []
    
    for i in range(0, len(words), chunk_size - overlap):
        chunk = ' '.join(words[i:i + chunk_size])
        if chunk.strip():
            chunks.append(chunk)
    
    return chunks

def create_embeddings(chunks):
    """Create embeddings for document chunks"""
    if embedding_model is None:
        return None
    
    try:
        # Process in smaller batches on CPU
        batch_size = 32
        embeddings = []
        
        for i in range(0, len(chunks), batch_size):
            batch = chunks[i:i + batch_size]
            batch_embeddings = embedding_model.encode(batch, show_progress_bar=False)
            embeddings.extend(batch_embeddings)
        
        return np.array(embeddings)
    except Exception as e:
        print(f"Error creating embeddings: {e}")
        return None

def retrieve_relevant_chunks(question, chunks, embeddings, top_k=3):
    """Retrieve most relevant chunks for a question"""
    if embedding_model is None or embeddings is None:
        return chunks[:3]  # Fallback to first 3 chunks
    
    try:
        question_embedding = embedding_model.encode([question], show_progress_bar=False)
        similarities = cosine_similarity(question_embedding, embeddings)[0]
        
        # Get top-k most similar chunks
        top_indices = np.argsort(similarities)[-top_k:][::-1]
        relevant_chunks = [chunks[i] for i in top_indices]
        
        return relevant_chunks
    except Exception as e:
        print(f"Error retrieving chunks: {e}")
        return chunks[:3]  # Fallback

def process_uploaded_pdf(pdf_file, progress=gr.Progress()):
    """Main processing function for uploaded PDF"""
    global processed_markdown, show_results_tab, document_chunks, document_embeddings
    
    if pdf_file is None:
        return "❌ No PDF uploaded", gr.Tabs(visible=False)
    
    try:
        # Process PDF
        progress(0.1, desc="Processing PDF...")
        combined_markdown, status = process_pdf_document(pdf_file, dolphin_model, progress)
        
        if status == "processing_complete":
            processed_markdown = combined_markdown
            
            # Create chunks and embeddings for RAG
            progress(0.9, desc="Creating document chunks for RAG...")
            document_chunks = chunk_document(processed_markdown)
            document_embeddings = create_embeddings(document_chunks)
            print(f"Created {len(document_chunks)} chunks")
            
            show_results_tab = True
            progress(1.0, desc="PDF processed successfully!")
            return "✅ PDF processed successfully! Chatbot is ready in the Chat tab.", gr.Tabs(visible=True)
        else:
            show_results_tab = False
            return combined_markdown, gr.Tabs(visible=False)
            
    except Exception as e:
        show_results_tab = False
        error_msg = f"❌ Error processing PDF: {str(e)}"
        return error_msg, gr.Tabs(visible=False)


def get_processed_markdown():
    """Return the processed markdown content"""
    global processed_markdown
    return processed_markdown if processed_markdown else "No document processed yet."


def clear_all():
    """Clear all data and hide results tab"""
    global processed_markdown, show_results_tab, document_chunks, document_embeddings
    processed_markdown = ""
    show_results_tab = False
    document_chunks = []
    document_embeddings = None
    
    # Clear GPU cache
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    
    return None, "", gr.Tabs(visible=False)


# Create Gradio interface
with gr.Blocks(
    title="DOLPHIN PDF AI - Local Gemma 3n", 
    theme=gr.themes.Soft(),
    css="""
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
    
    * {
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif !important;
    }
    
    .main-container { 
        max-width: 1000px; 
        margin: 0 auto; 
    }
    .upload-container { 
        text-align: center; 
        padding: 40px 20px;
        border: 2px dashed #e0e0e0;
        border-radius: 15px;
        margin: 20px 0;
    }
    .upload-button {
        font-size: 18px !important;
        padding: 15px 30px !important;
        margin: 20px 0 !important;
        font-weight: 600 !important;
    }
    .status-message {
        text-align: center;
        padding: 15px;
        margin: 10px 0;
        border-radius: 8px;
        font-weight: 500;
    }
    .chatbot-container {
        max-height: 600px;
    }
    h1, h2, h3 {
        font-weight: 700 !important;
    }
    #progress-container {
        margin: 10px 0;
        min-height: 20px;
    }
    """
) as demo:
    
    with gr.Tabs() as main_tabs:
        # Home Tab
        with gr.TabItem("🏠 Home", id="home"):
            embedding_status = "✅ RAG ready" if embedding_model else "❌ RAG not loaded"
            gr.Markdown(
                "# Scholar Express - Local Gemma 3n Version\n"
                "### Upload a research paper to get a web-friendly version with AI-generated alt text for accessibility. Includes an AI chatbot powered by local Gemma 3n.\n"
                f"**System:** {model_status}\n"
                f"**RAG System:** {embedding_status}\n"
                f"**DOLPHIN:** Local model for PDF processing\n"
                f"**Gemma 3n:** Local model for alt text generation and chat\n"
                f"**Alt Text:** Gemma 3n generates descriptive alt text for images\n"
                f"**GPU:** {'CUDA available' if torch.cuda.is_available() else 'CPU only'}"
            )
            
            with gr.Column(elem_classes="upload-container"):
                gr.Markdown("## 📄 Upload Your PDF Document")
                
                pdf_input = gr.File(
                    file_types=[".pdf"],
                    label="",
                    height=150,
                    elem_id="pdf_upload"
                )
                
                process_btn = gr.Button(
                    "🚀 Process PDF", 
                    variant="primary", 
                    size="lg",
                    elem_classes="upload-button"
                )
                
                clear_btn = gr.Button(
                    "🗑️ Clear", 
                    variant="secondary"
                )
            
            # Dedicated progress space
            progress_space = gr.HTML(
                value="",
                visible=False,
                elem_id="progress-container"
            )
            
            # Status output (hidden during processing)
            status_output = gr.Markdown(
                "",
                elem_classes="status-message"
            )
        
        # Results Tab (initially hidden)
        with gr.TabItem("📖 Document", id="results", visible=False) as results_tab:
            gr.Markdown("## Processed Document")
            
            markdown_display = gr.Markdown(
                value="",
                latex_delimiters=[
                    {"left": "$$", "right": "$$", "display": True},
                    {"left": "$", "right": "$", "display": False}
                ],
                height=700
            )
        
        # Chatbot Tab (initially hidden)
        with gr.TabItem("💬 Chat", id="chat", visible=False) as chat_tab:
            gr.Markdown("## Ask Questions About Your Document")
            
            chatbot = gr.Chatbot(
                value=[],
                height=500,
                elem_classes="chatbot-container",
                placeholder="Your conversation will appear here once you process a document..."
            )
            
            with gr.Row():
                msg_input = gr.Textbox(
                    placeholder="Ask a question about the processed document...",
                    scale=4,
                    container=False
                )
                send_btn = gr.Button("Send", variant="primary", scale=1)
            
            gr.Markdown(
                "*Ask questions about your processed document. The AI uses RAG (Retrieval-Augmented Generation) with local Gemma 3n to find relevant sections and provide accurate answers.*",
                elem_id="chat-notice"
            )
    
    # Event handlers
    process_btn.click(
        fn=process_uploaded_pdf,
        inputs=[pdf_input],
        outputs=[status_output, results_tab],
        show_progress=True
    ).then(
        fn=get_processed_markdown,
        outputs=[markdown_display]
    ).then(
        fn=lambda: gr.TabItem(visible=True),
        outputs=[chat_tab]
    )
    
    clear_btn.click(
        fn=clear_all,
        outputs=[pdf_input, status_output, results_tab]
    ).then(
        fn=lambda: gr.HTML(visible=False),
        outputs=[progress_space]
    ).then(
        fn=lambda: gr.TabItem(visible=False),
        outputs=[chat_tab]
    )
    
    # Chatbot functionality with local Gemma 3n
    def chatbot_response(message, history):
        if not message.strip():
            return history
        
        if not processed_markdown:
            return history + [[message, "❌ Please process a PDF document first before asking questions."]]
        
        try:
            # Use RAG to get relevant chunks from markdown
            if document_chunks and len(document_chunks) > 0:
                relevant_chunks = retrieve_relevant_chunks(message, document_chunks, document_embeddings, top_k=3)
                context = "\n\n".join(relevant_chunks)
                # Smart truncation: aim for ~6000 chars for local model
                if len(context) > 6000:
                    # Try to cut at sentence boundaries
                    sentences = context[:6000].split('.')
                    context = '.'.join(sentences[:-1]) + '...' if len(sentences) > 1 else context[:6000] + '...'
            else:
                # Fallback to truncated document if RAG fails
                context = processed_markdown[:6000] + "..." if len(processed_markdown) > 6000 else processed_markdown
            
            # Create prompt for Gemma 3n
            prompt = f"""You are a helpful assistant that answers questions about documents. Use the provided context to answer questions accurately and concisely.

Context from the document:
{context}

Question: {message}

Please provide a clear and helpful answer based on the context provided."""
            
            # Generate response using local Gemma 3n
            response_text = gemma_model.chat(prompt)
            return history + [[message, response_text]]
            
        except Exception as e:
            error_msg = f"❌ Error generating response: {str(e)}"
            print(f"Full error: {e}")
            import traceback
            traceback.print_exc()
            return history + [[message, error_msg]]
    
    send_btn.click(
        fn=chatbot_response,
        inputs=[msg_input, chatbot],
        outputs=[chatbot]
    ).then(
        lambda: "",
        outputs=[msg_input]
    )
    
    # Also allow Enter key to send message
    msg_input.submit(
        fn=chatbot_response,
        inputs=[msg_input, chatbot],
        outputs=[chatbot]
    ).then(
        lambda: "",
        outputs=[msg_input]
    )


if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        max_threads=4,
        inbrowser=False,
        quiet=True
    )