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)