main.py

from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from transformers import pipeline
from typing import Tuple, Optional
import io
import fitz  # PyMuPDF
from PIL import Image
import pandas as pd
import uvicorn
from docx import Document
from pptx import Presentation
import pytesseract
import logging
import re
from slowapi import Limiter
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded
from slowapi.middleware import SlowAPIMiddleware
import matplotlib.pyplot as plt
import seaborn as sns
import tempfile
import base64
from io import BytesIO
from pydantic import BaseModel
import traceback
import ast
from fastapi.responses import HTMLResponse
from fastapi import Request
from pathlib import Path
from fastapi.staticfiles import StaticFiles


# Initialize rate limiter
limiter = Limiter(key_func=get_remote_address)

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI()


# Serve static files (frontend)
app.mount("/static", StaticFiles(directory="static"), name="static")


@app.get("/", response_class=HTMLResponse)
def home ():
    with open("static/indexAI.html","r") as file :
        return file.read()

        
# Apply rate limiting middleware
app.state.limiter = limiter
app.add_middleware(SlowAPIMiddleware)

# CORS Configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# Constants
MAX_FILE_SIZE = 10 * 1024 * 1024  # 10MB
SUPPORTED_FILE_TYPES = {
    "docx", "xlsx", "pptx", "pdf", "jpg", "jpeg", "png"
}

# Model caching
summarizer = None
qa_model = None
image_captioner = None

def get_summarizer():
    global summarizer
    if summarizer is None:
        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    return summarizer

def get_qa_model():
    global qa_model
    if qa_model is None:
        qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
    return qa_model

def get_image_captioner():
    global image_captioner
    if image_captioner is None:
        image_captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
    return image_captioner

async def process_uploaded_file(file: UploadFile) -> Tuple[str, bytes]:
    """Validate and process uploaded file with special handling for each type"""
    if not file.filename:
        raise HTTPException(400, "No filename provided")

    file_ext = file.filename.split('.')[-1].lower()
    if file_ext not in SUPPORTED_FILE_TYPES:
        raise HTTPException(400, f"Unsupported file type. Supported: {', '.join(SUPPORTED_FILE_TYPES)}")

    content = await file.read()
    if len(content) > MAX_FILE_SIZE:
        raise HTTPException(413, f"File too large. Max size: {MAX_FILE_SIZE//1024//1024}MB")

    # Special validation for PDFs
    if file_ext == "pdf":
        try:
            with fitz.open(stream=content, filetype="pdf") as doc:
                if doc.is_encrypted:
                    if not doc.authenticate(""):
                        raise ValueError("Encrypted PDF - cannot extract text")
                if len(doc) > 50:
                    raise ValueError("PDF too large (max 50 pages)")
        except Exception as e:
            logger.error(f"PDF validation failed: {str(e)}")
            raise HTTPException(422, detail=f"Invalid PDF file: {str(e)}")

    await file.seek(0)  # Reset file pointer for processing
    return file_ext, content

def extract_text(content: bytes, file_ext: str) -> str:
    """Extract text from various file formats with enhanced support"""
    try:
        if file_ext == "docx":
            doc = Document(io.BytesIO(content))
            return "\n".join(para.text for para in doc.paragraphs if para.text.strip())
        
        elif file_ext in {"xlsx", "xls"}:
            df = pd.read_excel(io.BytesIO(content), sheet_name=None)
            all_text = []
            for sheet_name, sheet_data in df.items():
                sheet_text = []
                for column in sheet_data.columns:
                    sheet_text.extend(sheet_data[column].dropna().astype(str).tolist())
                all_text.append(f"Sheet: {sheet_name}\n" + "\n".join(sheet_text))
            return "\n\n".join(all_text)
        
        elif file_ext == "pptx":
            ppt = Presentation(io.BytesIO(content))
            text = []
            for slide in ppt.slides:
                for shape in slide.shapes:
                    if hasattr(shape, "text") and shape.text.strip():
                        text.append(shape.text)
            return "\n".join(text)
        
        elif file_ext == "pdf":
            pdf = fitz.open(stream=content, filetype="pdf")
            return "\n".join(page.get_text("text") for page in pdf)
        
        elif file_ext in {"jpg", "jpeg", "png"}:
            # First try OCR
            try:
                image = Image.open(io.BytesIO(content))
                text = pytesseract.image_to_string(image, config='--psm 6')
                if text.strip():
                    return text
                
                # If OCR fails, try image captioning
                captioner = get_image_captioner()
                result = captioner(image)
                return result[0]['generated_text']
            except Exception as img_e:
                logger.error(f"Image processing failed: {str(img_e)}")
                raise ValueError("Could not extract text or caption from image")
        
    except Exception as e:
        logger.error(f"Text extraction failed for {file_ext}: {str(e)}")
        raise HTTPException(422, f"Failed to extract text from {file_ext} file")

# Visualization Models
class VisualizationRequest(BaseModel):
    chart_type: str
    x_column: Optional[str] = None
    y_column: Optional[str] = None
    hue_column: Optional[str] = None
    title: Optional[str] = None
    x_label: Optional[str] = None
    y_label: Optional[str] = None
    style: str = "seaborn-v0_8"  # Updated default
    filters: Optional[dict] = None

class NaturalLanguageRequest(BaseModel):
    prompt: str
    style: str = "seaborn-v0_8"

def validate_matplotlib_style(style: str) -> str:
    """Validate and return a valid matplotlib style"""
    available_styles = plt.style.available
    # Map legacy style names to current ones
    style_mapping = {
        'seaborn': 'seaborn-v0_8',
        'seaborn-white': 'seaborn-v0_8-white',
        'seaborn-dark': 'seaborn-v0_8-dark',
        # Add other legacy mappings if needed
    }
    
    # Check if it's a legacy name we can map
    if style in style_mapping:
        return style_mapping[style]
    
    # Check if it's a valid current style
    if style in available_styles:
        return style
    
    logger.warning(f"Invalid style '{style}'. Available styles: {available_styles}")
    return "seaborn-v0_8"  # Default fallback to current seaborn style



    
def generate_visualization_code(df: pd.DataFrame, request: VisualizationRequest) -> str:
    """Generate Python code for visualization based on request parameters"""
    # Validate style
    valid_style = validate_matplotlib_style(request.style)
    
    code_lines = [
        "import matplotlib.pyplot as plt",
        "import seaborn as sns",
        "import pandas as pd",
        "",
        "# Data preparation",
        f"df = pd.DataFrame({df.to_dict(orient='list')})",
    ]
    
    # Apply filters if specified
    if request.filters:
        filter_conditions = []
        for column, condition in request.filters.items():
            if isinstance(condition, dict):
                if 'min' in condition and 'max' in condition:
                    filter_conditions.append(f"(df['{column}'] >= {condition['min']}) & (df['{column}'] <= {condition['max']})")
                elif 'values' in condition:
                    values = ', '.join([f"'{v}'" if isinstance(v, str) else str(v) for v in condition['values']])
                    filter_conditions.append(f"df['{column}'].isin([{values}])")
            else:
                filter_conditions.append(f"df['{column}'] == {repr(condition)}")
        
        if filter_conditions:
            code_lines.extend([
                "",
                "# Apply filters",
                f"df = df[{' & '.join(filter_conditions)}]"
            ])
    
    code_lines.extend([
        "",
        "# Visualization",
        f"plt.style.use('{valid_style}')",
        f"plt.figure(figsize=(10, 6))"
    ])
    
    # Chart type specific code
    if request.chart_type == "line":
        if request.hue_column:
            code_lines.append(f"sns.lineplot(data=df, x='{request.x_column}', y='{request.y_column}', hue='{request.hue_column}')")
        else:
            code_lines.append(f"plt.plot(df['{request.x_column}'], df['{request.y_column}'])")
    elif request.chart_type == "bar":
        if request.hue_column:
            code_lines.append(f"sns.barplot(data=df, x='{request.x_column}', y='{request.y_column}', hue='{request.hue_column}')")
        else:
            code_lines.append(f"plt.bar(df['{request.x_column}'], df['{request.y_column}'])")
    elif request.chart_type == "scatter":
        if request.hue_column:
            code_lines.append(f"sns.scatterplot(data=df, x='{request.x_column}', y='{request.y_column}', hue='{request.hue_column}')")
        else:
            code_lines.append(f"plt.scatter(df['{request.x_column}'], df['{request.y_column}'])")
    elif request.chart_type == "histogram":
        code_lines.append(f"plt.hist(df['{request.x_column}'], bins=20)")
    elif request.chart_type == "boxplot":
        if request.hue_column:
            code_lines.append(f"sns.boxplot(data=df, x='{request.x_column}', y='{request.y_column}', hue='{request.hue_column}')")
        else:
            code_lines.append(f"sns.boxplot(data=df, x='{request.x_column}', y='{request.y_column}')")
    elif request.chart_type == "heatmap":
        code_lines.append(f"corr = df.corr()")
        code_lines.append(f"sns.heatmap(corr, annot=True, cmap='coolwarm')")
    else:
        raise ValueError(f"Unsupported chart type: {request.chart_type}")
    
    # Add labels and title
    if request.title:
        code_lines.append(f"plt.title('{request.title}')")
    if request.x_label:
        code_lines.append(f"plt.xlabel('{request.x_label}')")
    if request.y_label:
        code_lines.append(f"plt.ylabel('{request.y_label}')")
    
    code_lines.extend([
        "plt.tight_layout()",
        "plt.show()"
    ])
    
    return "\n".join(code_lines)

def interpret_natural_language(prompt: str, df_columns: list) -> VisualizationRequest:
    """Convert natural language prompt to visualization parameters"""
    prompt = prompt.lower()
    
    # Determine chart type
    chart_type = "bar"
    if "line" in prompt:
        chart_type = "line"
    elif "scatter" in prompt:
        chart_type = "scatter"
    elif "histogram" in prompt:
        chart_type = "histogram"
    elif "box" in prompt:
        chart_type = "boxplot"
    elif "heatmap" in prompt or "correlation" in prompt:
        chart_type = "heatmap"
    
    # Try to detect columns
    x_col = None
    y_col = None
    hue_col = None
    
    for col in df_columns:
        if col.lower() in prompt:
            if not x_col:
                x_col = col
            elif not y_col:
                y_col = col
            else:
                hue_col = col
    
    # Default to first columns if not detected
    if not x_col and len(df_columns) > 0:
        x_col = df_columns[0]
    if not y_col and len(df_columns) > 1:
        y_col = df_columns[1]
    
    return VisualizationRequest(
        chart_type=chart_type,
        x_column=x_col,
        y_column=y_col,
        hue_column=hue_col,
        title="Generated from: " + prompt[:50] + ("..." if len(prompt) > 50 else ""),
        style="seaborn-v0_8"  # Updated default
    )

@app.post("/summarize")
@limiter.limit("5/minute")
async def summarize_document(request: Request, file: UploadFile = File(...)):
    try:
        file_ext, content = await process_uploaded_file(file)
        text = extract_text(content, file_ext)
        
        if not text.strip():
            raise HTTPException(400, "No extractable text found")
        
        # Clean and chunk text
        text = re.sub(r'\s+', ' ', text).strip()
        chunks = [text[i:i+1000] for i in range(0, len(text), 1000)]
        
        # Summarize each chunk
        summarizer = get_summarizer()
        summaries = []
        for chunk in chunks:
            summary = summarizer(chunk, max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
            summaries.append(summary)
        
        return {"summary": " ".join(summaries)}
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Summarization failed: {str(e)}")
        raise HTTPException(500, "Document summarization failed")

@app.post("/qa")
@limiter.limit("5/minute")
async def question_answering(
    request: Request,
    file: UploadFile = File(...),
    question: str = Form(...),
    language: str = Form("fr")
):
    try:
        file_ext, content = await process_uploaded_file(file)
        text = extract_text(content, file_ext)
        
        if not text.strip():
            raise HTTPException(400, "No extractable text found")

        # Clean and truncate text
        text = re.sub(r'\s+', ' ', text).strip()[:5000]

        # Theme detection
        theme_keywords = ["thème", "sujet principal", "quoi le sujet", "theme", "main topic"]
        if any(kw in question.lower() for kw in theme_keywords):
            try:
                summarizer = get_summarizer()
                summary_output = summarizer(
                    text,
                    max_length=min(100, len(text)//4),
                    min_length=30,
                    do_sample=False,
                    truncation=True
                )
                
                theme = summary_output[0].get("summary_text", text[:200] + "...")
                return {
                    "question": question,
                    "answer": f"Le document traite principalement de : {theme}",
                    "confidence": 0.95,
                    "language": language
                }
            except Exception:
                theme = text[:200] + ("..." if len(text) > 200 else "")
                return {
                    "question": question,
                    "answer": f"D'après le document : {theme}",
                    "confidence": 0.7,
                    "language": language,
                    "warning": "theme_summary_fallback"
                }

        # Standard QA
        qa = get_qa_model()
        result = qa(question=question, context=text[:3000])
        
        return {
            "question": question,
            "answer": result["answer"],
            "confidence": result["score"],
            "language": language
        }

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"QA processing failed: {str(e)}")
        raise HTTPException(500, detail=f"Analysis failed: {str(e)}")

@app.post("/visualize/code")
@limiter.limit("5/minute")
async def visualize_with_code(
    request: Request,
    file: UploadFile = File(...),
    chart_type: str = Form(...),
    x_column: Optional[str] = Form(None),
    y_column: Optional[str] = Form(None),
    hue_column: Optional[str] = Form(None),
    title: Optional[str] = Form(None),
    x_label: Optional[str] = Form(None),
    y_label: Optional[str] = Form(None),
    style: str = Form("seaborn-v0_8"),  # Updated default
    filters: Optional[str] = Form(None)
):
    try:
        file_ext, content = await process_uploaded_file(file)

        if file_ext not in {"xlsx", "xls"}:
            raise HTTPException(400, "Visualization is only supported for Excel files")

        df = pd.read_excel(io.BytesIO(content))

        if df.empty:
            raise HTTPException(400, "The uploaded Excel file is empty")

        # Convert filters from string to dictionary safely
        filters_dict = None
        if filters:
            try:
                filters_dict = ast.literal_eval(filters)
                if not isinstance(filters_dict, dict):
                    raise ValueError()
            except Exception:
                raise HTTPException(400, "Invalid format for filters. Must be a valid dictionary string.")

        viz_request = VisualizationRequest(
            chart_type=chart_type,
            x_column=x_column,
            y_column=y_column,
            hue_column=hue_column,
            title=title,
            x_label=x_label,
            y_label=y_label,
            style=style,
            filters=filters_dict
        )

        code = generate_visualization_code(df, viz_request)
        return {"code": code}

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Visualization code generation failed: {str(e)}")
        raise HTTPException(500, f"Visualization code generation failed: {str(e)}")

from fastapi.responses import FileResponse  # Add this import at the top

@app.post("/visualize/natural")
@limiter.limit("5/minute")
async def visualize_with_natural_language(
    request: Request,
    file: UploadFile = File(...),
    prompt: str = Form(...),
    style: str = Form("seaborn-v0_8"),
    return_type: str = Form("base64")  # New parameter: "base64" or "file"
):
    try:
        # Validate file and process data (existing code)
        file_ext, content = await process_uploaded_file(file)
        if file_ext not in {"xlsx", "xls"}:
            raise HTTPException(400, "Only Excel files are supported for visualization")
        
        df = pd.read_excel(io.BytesIO(content))
        nl_request = NaturalLanguageRequest(prompt=prompt, style=style)
        vis_request = interpret_natural_language(nl_request.prompt, df.columns.tolist())
        visualization_code = generate_visualization_code(df, vis_request)

        # Generate the plot
        plt.figure()
        local_vars = {}
        exec(visualization_code, globals(), local_vars)
        
        # Save the plot to a temporary file
        temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
        plt.savefig(temp_file.name, format='png', dpi=150, bbox_inches='tight')
        plt.close()

        # Handle response type
        if return_type == "file":
            # Return as downloadable file
            return FileResponse(
                temp_file.name,
                media_type="image/png",
                filename="visualization.png"
            )
        else:
            # Return as Base64 (original behavior)
            with open(temp_file.name, "rb") as f:
                image_bytes = f.read()
            image_base64 = base64.b64encode(image_bytes).decode('utf-8')
            
            # Clean up the temp file
            try:
                os.unlink(temp_file.name)
            except:
                pass
            
            return {
                "status": "success",
                "image": f"data:image/png;base64,{image_base64}",
                "code": visualization_code,
                "interpreted_parameters": vis_request.dict()
            }

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Natural language visualization failed: {str(e)}\n{traceback.format_exc()}")
        raise HTTPException(500, detail=f"Visualization failed: {str(e)}")

@app.get("/visualize/styles")
@limiter.limit("10/minute")
async def list_available_styles(request: Request):
    """List all available matplotlib styles"""
    return {"available_styles": plt.style.available}

@app.post("/get_columns")
@limiter.limit("10/minute")
async def get_excel_columns(
    request: Request,
    file: UploadFile = File(...)
):
    try:
        file_ext, content = await process_uploaded_file(file)
        if file_ext not in {"xlsx", "xls"}:
            raise HTTPException(400, "Only Excel files are supported")
        
        df = pd.read_excel(io.BytesIO(content))
        return {
            "columns": list(df.columns),
            "sample_data": df.head().to_dict(orient='records'),
            "statistics": df.describe().to_dict() if len(df.select_dtypes(include=['number']).columns) > 0 else None
        }
    except Exception as e:
        logger.error(f"Column extraction failed: {str(e)}")
        raise HTTPException(500, detail="Failed to extract columns from Excel file")

@app.exception_handler(RateLimitExceeded)
async def rate_limit_exceeded_handler(request: Request, exc: RateLimitExceeded):
    return JSONResponse(
        status_code=429,
        content={"detail": "Too many requests. Please try again later."}
    )
import gradio as gr

# Gradio interface for visualization
def gradio_visualize(file, prompt, style="seaborn-v0_8"):
    # Call your existing FastAPI endpoint
    with open(file.name, "rb") as f:
        response = client.post(
            "/visualize/natural",
            files={"file": f},
            data={"prompt": prompt, "style": style}
        )
    result = response.json()
    
    # Return both image and code
    return (
        result["image"],  # Base64 image
        f"```python\n{result['code']}\n```"  # Code with Markdown formatting
    )

# Create Gradio interface
visualization_interface = gr.Interface(
    fn=gradio_visualize,
    inputs=[
        gr.File(label="Upload Excel File", type="filepath"),
        gr.Textbox(label="Visualization Prompt", placeholder="e.g., 'Show sales by region'"),
        gr.Dropdown(label="Style", choices=plt.style.available, value="seaborn-v0_8")
    ],
    outputs=[
        gr.Image(label="Generated Visualization"),  # Auto-handles base64
        gr.Markdown(label="Generated Code")  # Renders code with syntax highlighting
    ],
    title="📊 Data Visualizer",
    description="Upload an Excel file and describe the visualization you want"
)

# Mount Gradio to your FastAPI app
app = gr.mount_gradio_app(app, visualization_interface, path="/gradio")





# ===== ADD THIS AT THE BOTTOM OF main.py =====
if __name__ == "__main__":
    import uvicorn
    from fastapi.testclient import TestClient
    from io import BytesIO
    import base64
    from PIL import Image
    import matplotlib.pyplot as plt

    # 1. Start the app (or connect to a running instance)
    client = TestClient(app)

    # 2. Test the visualization endpoint
    test_file = "test.xlsx"  # Replace with your test file
    test_prompt = "Show me a bar chart of sales by region"

    # 3. Send request to your own API
    with open(test_file, "rb") as f:
        response = client.post(
            "/visualize/natural",
            files={"file": ("test.xlsx", f, "application/vnd.ms-excel")},
            data={"prompt": test_prompt}
        )

    # 4. Check if successful
    if response.status_code == 200:
        result = response.json()
        print("Visualization generated successfully!")

        # 5. Decode and display the image
        image_data = result["image"].split(",")[1]  # Remove header
        image_bytes = base64.b64decode(image_data)
        image = Image.open(BytesIO(image_bytes))

        plt.imshow(image)
        plt.axis("off")
        plt.show()
    else:
        print(f"Error: {response.status_code}\n{response.text}")

    # 6. Optional: Run the server (if not already running)
    uvicorn.run(app, host="0.0.0.0", port=7860)