diff --git a/main.py b/main.py
new file mode 100644
index 0000000..aa45f00
--- /dev/null
+++ b/main.py
@@ -0,0 +1,176 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from doctr.models import ocr_predictor
+from doctr.io import DocumentFile
+import tempfile
+import os
+from typing import List
+import uvicorn
+
+app = FastAPI(
+    title="OCR API",
+    description="Extract text from images using DocTR",
+    version="1.0.0"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Initialize the model once at startup
+model = None
+
+@app.on_event("startup")
+async def load_model():
+    global model
+    print("Loading OCR model...")
+    model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
+    print("Model loaded successfully!")
+
+@app.get("/")
+async def root():
+    return {
+        "message": "OCR API is running",
+        "endpoints": {
+            "POST /ocr": "Extract text from a single image",
+            "POST /ocr/batch": "Extract text from multiple images"
+        }
+    }
+
+@app.post("/ocr")
+async def extract_text(file: UploadFile = File(...)):
+    """
+    Extract text from a single image file.
+    
+    Returns:
+        - text: Extracted text as a single line
+        - word_count: Number of words extracted
+    """
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File must be an image")
+    
+    try:
+        # Save uploaded file temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as tmp:
+            content = await file.read()
+            tmp.write(content)
+            tmp_path = tmp.name
+        
+        # Process the image
+        doc = DocumentFile.from_images(tmp_path)
+        result = model(doc)
+        
+        # Extract all words
+        all_words = []
+        for page in result.pages:
+            for block in page.blocks:
+                for line in block.lines:
+                    for word in line.words:
+                        all_words.append(word.value)
+        
+        # Join as single line
+        single_line = ' '.join(all_words)
+        
+        # Clean up temp file
+        os.unlink(tmp_path)
+        
+        return JSONResponse({
+            "success": True,
+            "text": single_line,
+            "word_count": len(all_words)
+        })
+    
+    except Exception as e:
+        # Clean up temp file if it exists
+        if 'tmp_path' in locals():
+            try:
+                os.unlink(tmp_path)
+            except:
+                pass
+        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
+
+@app.post("/ocr/batch")
+async def extract_text_batch(files: List[UploadFile] = File(...)):
+    """
+    Extract text from multiple image files.
+    
+    Returns:
+        - results: List of extracted texts with metadata
+    """
+    if len(files) > 10:
+        raise HTTPException(status_code=400, detail="Maximum 10 files allowed per batch")
+    
+    results = []
+    
+    for file in files:
+        if not file.content_type.startswith('image/'):
+            results.append({
+                "filename": file.filename,
+                "success": False,
+                "error": "File must be an image"
+            })
+            continue
+        
+        try:
+            # Save uploaded file temporarily
+            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as tmp:
+                content = await file.read()
+                tmp.write(content)
+                tmp_path = tmp.name
+            
+            # Process the image
+            doc = DocumentFile.from_images(tmp_path)
+            result = model(doc)
+            
+            # Extract all words
+            all_words = []
+            for page in result.pages:
+                for block in page.blocks:
+                    for line in block.lines:
+                        for word in line.words:
+                            all_words.append(word.value)
+            
+            # Join as single line
+            single_line = ' '.join(all_words)
+            
+            # Clean up temp file
+            os.unlink(tmp_path)
+            
+            results.append({
+                "filename": file.filename,
+                "success": True,
+                "text": single_line,
+                "word_count": len(all_words)
+            })
+        
+        except Exception as e:
+            # Clean up temp file if it exists
+            if 'tmp_path' in locals():
+                try:
+                    os.unlink(tmp_path)
+                except:
+                    pass
+            results.append({
+                "filename": file.filename,
+                "success": False,
+                "error": str(e)
+            })
+    
+    return JSONResponse({"results": results})
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8999)
\ No newline at end of file