Update app.py

app.py

@@ -1,204 +1,206 @@
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-import uvicorn
-import os
-import tempfile
-import aiofiles
-from datetime import datetime
-import traceback
-import logging
-from typing import List, Optional
-
-# Setup logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-app = FastAPI(title="Material Summarizer API")
-
-from dotenv import load_dotenv
-load_dotenv()
-
-# Get URLs from environment
-FRONTEND_URL = os.getenv('FRONTEND_URL')
-BACKEND_URL = os.getenv('BACKEND_URL', 'http://localhost:5000')
-
-# CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["FRONTEND_URL, BACKEND_URL"],  # Adjust in production
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Import processing functions
-try:
-    from document_parser import parse_document
-    from summarizer import summarize_text
-    from utils import chunked_summarize
-    DEPENDENCIES_LOADED = True
-    logger.info("All AI dependencies loaded successfully")
-except ImportError as e:
-    logger.error(f"Import error: {e}")
-    DEPENDENCIES_LOADED = False
-
-@app.get("/")
-async def root():
-    return {"message": "Material Summarizer API", "status": "running"}
-
-@app.get("/health")
-async def health_check():
-    status = "healthy" if DEPENDENCIES_LOADED else "missing_dependencies"
-    return {
-        "status": status,
-        "service": "material-summarizer",
-        "dependencies_loaded": DEPENDENCIES_LOADED
-    }
-
-@app.post("/summarize-document")
-async def summarize_document(
-    file: UploadFile = File(...),
-    max_summary_length: Optional[int] = 1000,
-    chunk_size: Optional[int] = 1500
-):
-    """
-    Summarize uploaded document (PDF, DOCX, TXT, etc.)
-    """
-    if not DEPENDENCIES_LOADED:
-        raise HTTPException(
-            status_code=500,
-            detail="Required AI dependencies not loaded. Check server logs."
-        )
-
-    temp_file_path = None
-
-    try:
-        # Validate file type
-        allowed_extensions = {'.pdf', '.docx', '.doc', '.txt', '.pptx', '.ppt'}
-        file_extension = os.path.splitext(file.filename)[1].lower()
-        
-        if file_extension not in allowed_extensions:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Unsupported document format. Allowed: {', '.join(allowed_extensions)}"
-            )
-
-        # Create temporary file
-        temp_file_path = f"temp_{file.filename}"
-
-        # Save uploaded file
-        logger.info(f"Saving uploaded file: {file.filename}")
-        async with aiofiles.open(temp_file_path, 'wb') as out_file:
-            content = await file.read()
-            await out_file.write(content)
-
-        start_time = datetime.now()
-
-        # 1. Parse document
-        logger.info("Step 1: Parsing document...")
-        if not os.path.exists(temp_file_path):
-            raise HTTPException(status_code=500, detail="Document file not found after upload")
-
-        document_text = parse_document(temp_file_path, file_extension)
-        logger.info(f"Extracted text length: {len(document_text)} characters")
-
-        if not document_text or len(document_text.strip()) < 10:
-            raise HTTPException(status_code=500, detail="Document parsing failed or content too short")
-
-        # 2. Summarize text with chunking
-        logger.info("Step 2: Generating summary...")
-        
-        def custom_summarize_func(text):
-            return summarize_text(
-                text, 
-                model_name="facebook/bart-large-cnn",
-                max_length=max_summary_length,
-                min_length=min(100, max_summary_length // 3)
-            )
-
-        final_summary = chunked_summarize(
-            text=document_text,
-            summarize_func=custom_summarize_func,
-            max_chunk_size=chunk_size
-        )
-
-        if not final_summary or len(final_summary.strip()) < 10:
-            raise HTTPException(status_code=500, detail="Summary generation failed")
-
-        processing_time = (datetime.now() - start_time).total_seconds()
-
-        logger.info(f"Summarization completed in {processing_time:.2f} seconds")
-
-        return {
-            "success": True,
-            "summary": final_summary,
-            "original_length": len(document_text),
-            "summary_length": len(final_summary),
-            "processing_time": processing_time,
-            "file_type": file_extension
-        }
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error processing document: {str(e)}")
-        logger.error(traceback.format_exc())
-        raise HTTPException(
-            status_code=500,
-            detail=f"Document processing failed: {str(e)}"
-        )
-    finally:
-        # Cleanup temporary files
-        try:
-            if temp_file_path and os.path.exists(temp_file_path):
-                os.remove(temp_file_path)
-                logger.info(f"Cleaned up: {temp_file_path}")
-        except Exception as cleanup_error:
-            logger.error(f"Cleanup error: {cleanup_error}")
-
-@app.post("/batch-summarize")
-async def batch_summarize_documents(files: List[UploadFile] = File(...)):
-    """
-    Summarize multiple documents in batch
-    """
-    if not DEPENDENCIES_LOADED:
-        raise HTTPException(
-            status_code=500,
-            detail="Required AI dependencies not loaded. Check server logs."
-        )
-
-    results = []
-    
-    for file in files:
-        try:
-            # Use the single document summarization function
-            result = await summarize_document(file)
-            result["filename"] = file.filename
-            results.append(result)
-        except Exception as e:
-            results.append({
-                "success": False,
-                "filename": file.filename,
-                "error": str(e)
-            })
-
-    return {
-        "success": True,
-        "processed_files": len(results),
-        "results": results
-    }
-
-if __name__ == "__main__":
-    logger.info("Starting Material Summarizer Server...")
-    logger.info("Dependencies loaded: %s", DEPENDENCIES_LOADED)
-
-    if not DEPENDENCIES_LOADED:
-        logger.error("CRITICAL: AI dependencies not loaded. Document processing will not work!")
-
-    port = int(os.environ.get("MATERIAL_PORT", 7861))
-    uvicorn.run(
-        "app:app",
-        host="0.0.0.0",
-        port=port,
-        reload=False
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import warnings
+warnings.filterwarnings("ignore", message=".*_pytree_node.*")
+import uvicorn
+import os
+import tempfile
+import aiofiles
+from datetime import datetime
+import traceback
+import logging
+from typing import List, Optional
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="Material Summarizer API")
+
+from dotenv import load_dotenv
+load_dotenv()
+
+# Get URLs from environment
+FRONTEND_URL = os.getenv('FRONTEND_URL')
+BACKEND_URL = os.getenv('BACKEND_URL', 'http://localhost:5000')
+
+# CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["FRONTEND_URL, BACKEND_URL"],  # Adjust in production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Import processing functions
+try:
+    from document_parser import parse_document
+    from summarizer import summarize_text
+    from utils import chunked_summarize
+    DEPENDENCIES_LOADED = True
+    logger.info("All AI dependencies loaded successfully")
+except ImportError as e:
+    logger.error(f"Import error: {e}")
+    DEPENDENCIES_LOADED = False
+
+@app.get("/")
+async def root():
+    return {"message": "Material Summarizer API", "status": "running"}
+
+@app.get("/health")
+async def health_check():
+    status = "healthy" if DEPENDENCIES_LOADED else "missing_dependencies"
+    return {
+        "status": status,
+        "service": "material-summarizer",
+        "dependencies_loaded": DEPENDENCIES_LOADED
+    }
+
+@app.post("/summarize-document")
+async def summarize_document(
+    file: UploadFile = File(...),
+    max_summary_length: Optional[int] = 1000,
+    chunk_size: Optional[int] = 1500
+):
+    """
+    Summarize uploaded document (PDF, DOCX, TXT, etc.)
+    """
+    if not DEPENDENCIES_LOADED:
+        raise HTTPException(
+            status_code=500,
+            detail="Required AI dependencies not loaded. Check server logs."
+        )
+
+    temp_file_path = None
+
+    try:
+        # Validate file type
+        allowed_extensions = {'.pdf', '.docx', '.doc', '.txt', '.pptx', '.ppt'}
+        file_extension = os.path.splitext(file.filename)[1].lower()
+        
+        if file_extension not in allowed_extensions:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Unsupported document format. Allowed: {', '.join(allowed_extensions)}"
+            )
+
+        # Create temporary file
+        temp_file_path = f"temp_{file.filename}"
+
+        # Save uploaded file
+        logger.info(f"Saving uploaded file: {file.filename}")
+        async with aiofiles.open(temp_file_path, 'wb') as out_file:
+            content = await file.read()
+            await out_file.write(content)
+
+        start_time = datetime.now()
+
+        # 1. Parse document
+        logger.info("Step 1: Parsing document...")
+        if not os.path.exists(temp_file_path):
+            raise HTTPException(status_code=500, detail="Document file not found after upload")
+
+        document_text = parse_document(temp_file_path, file_extension)
+        logger.info(f"Extracted text length: {len(document_text)} characters")
+
+        if not document_text or len(document_text.strip()) < 10:
+            raise HTTPException(status_code=500, detail="Document parsing failed or content too short")
+
+        # 2. Summarize text with chunking
+        logger.info("Step 2: Generating summary...")
+        
+        def custom_summarize_func(text):
+            return summarize_text(
+                text, 
+                model_name="facebook/bart-large-cnn",
+                max_length=max_summary_length,
+                min_length=min(100, max_summary_length // 3)
+            )
+
+        final_summary = chunked_summarize(
+            text=document_text,
+            summarize_func=custom_summarize_func,
+            max_chunk_size=chunk_size
+        )
+
+        if not final_summary or len(final_summary.strip()) < 10:
+            raise HTTPException(status_code=500, detail="Summary generation failed")
+
+        processing_time = (datetime.now() - start_time).total_seconds()
+
+        logger.info(f"Summarization completed in {processing_time:.2f} seconds")
+
+        return {
+            "success": True,
+            "summary": final_summary,
+            "original_length": len(document_text),
+            "summary_length": len(final_summary),
+            "processing_time": processing_time,
+            "file_type": file_extension
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error processing document: {str(e)}")
+        logger.error(traceback.format_exc())
+        raise HTTPException(
+            status_code=500,
+            detail=f"Document processing failed: {str(e)}"
+        )
+    finally:
+        # Cleanup temporary files
+        try:
+            if temp_file_path and os.path.exists(temp_file_path):
+                os.remove(temp_file_path)
+                logger.info(f"Cleaned up: {temp_file_path}")
+        except Exception as cleanup_error:
+            logger.error(f"Cleanup error: {cleanup_error}")
+
+@app.post("/batch-summarize")
+async def batch_summarize_documents(files: List[UploadFile] = File(...)):
+    """
+    Summarize multiple documents in batch
+    """
+    if not DEPENDENCIES_LOADED:
+        raise HTTPException(
+            status_code=500,
+            detail="Required AI dependencies not loaded. Check server logs."
+        )
+
+    results = []
+    
+    for file in files:
+        try:
+            # Use the single document summarization function
+            result = await summarize_document(file)
+            result["filename"] = file.filename
+            results.append(result)
+        except Exception as e:
+            results.append({
+                "success": False,
+                "filename": file.filename,
+                "error": str(e)
+            })
+
+    return {
+        "success": True,
+        "processed_files": len(results),
+        "results": results
+    }
+
+if __name__ == "__main__":
+    logger.info("Starting Material Summarizer Server...")
+    logger.info("Dependencies loaded: %s", DEPENDENCIES_LOADED)
+
+    if not DEPENDENCIES_LOADED:
+        logger.error("CRITICAL: AI dependencies not loaded. Document processing will not work!")
+
+    port = int(os.environ.get("MATERIAL_PORT", 7861))
+    uvicorn.run(
+        "app:app",
+        host="0.0.0.0",
+        port=port,
+        reload=False
     )