update_new_new_new_new

HTR/strike.py

@@ -5,24 +5,29 @@ import os
 import cv2
 from transformers import AutoModelForImageClassification
 import logging
-from HTR.word import TEMP_IMAGES_DIR
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
 
 # Initialize model globally
 model = None
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+TEMP_IMAGES_DIR = None
 
 def initialize_model():
     global model
     if model is None:
         try:
+            logger.info("Initializing model...")
             model = AutoModelForImageClassification.from_pretrained("models/vit-base-beans")
             if torch.cuda.is_available():
                 model = model.to('cuda')
+                logger.info("Model moved to CUDA")
+            logger.info("Model initialized successfully")
         except Exception as e:
-            print(f"Error initializing model: {str(e)}")
+            logger.error(f"Error initializing model: {str(e)}")
             raise
 
 def image_preprocessing(image):
@@ -37,7 +42,7 @@ def image_preprocessing(image):
             images.append(binary_image)
         return images
     except Exception as e:
-        print(f"Error in image_preprocessing: {str(e)}")
+        logger.error(f"Error in image_preprocessing: {str(e)}")
         return []
 
 def predict_image(image_path, model):
@@ -56,7 +61,7 @@ def predict_image(image_path, model):
             predictions = model(images).logits.detach().cpu().numpy()
         return predictions
     except Exception as e:
-        print(f"Error in predict_image: {str(e)}")
+        logger.error(f"Error in predict_image: {str(e)}")
         return None
 
 def struck_images(image_paths):

HTR/word.py

@@ -6,14 +6,16 @@ import os
 import tempfile
 import logging
 
-# Set up logging
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
 logger = logging.getLogger(__name__)
 
-cordinates =[]
-     
-      
-      
+# Global variables
+TEMP_IMAGES_DIR = None
+cordinates = []
+
 def four_point_transform(image, pts):
     try:
         rect = pts

main.py

@@ -6,6 +6,8 @@ import sys
 import builtins
 from datetime import datetime
 from flask_cors import CORS
+from flask import Flask, request, jsonify, render_template
+from werkzeug.utils import secure_filename
 from dotenv import load_dotenv
 
 # Load environment variables
@@ -44,14 +46,14 @@ except Exception as e:
 
 # Set up logging to both file and console
 try:
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler(log_file),
-        logging.StreamHandler(sys.stdout)
-    ]
-)
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.FileHandler(log_file),
+            logging.StreamHandler(sys.stdout)
+        ]
+    )
 except Exception as e:
     print(f"Warning: Could not set up file logging: {e}")
     # Fallback to console-only logging
@@ -107,7 +109,7 @@ for data in required_nltk_data:
     try:
         log_print(f"Downloading NLTK data: {data}")
         nltk.download(data, download_dir=nltk_data_dir)
-except Exception as e:
+    except Exception as e:
         log_print(f"Error downloading NLTK data {data}: {e}", "ERROR")
         raise
 
@@ -124,8 +126,10 @@ from similarity_check.llm_based_scoring.llm import llm_score
 
 app = Flask(__name__)
 
-# Use the new upload directory
-UPLOAD_FOLDER = upload_dir
+# Create a temporary directory for file uploads
+UPLOAD_FOLDER = tempfile.mkdtemp()
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+logger.info(f"Using temporary upload folder: {UPLOAD_FOLDER}")
 
 # Configure CORS for Hugging Face
 CORS(app, resources={
@@ -155,11 +159,11 @@ def compute_answers():
             return jsonify({"error": "Missing query file"}), 400
         
         try:
-        queries = query_file.read().decode('utf-8').splitlines()
+            queries = query_file.read().decode('utf-8').splitlines()
             if not queries:
                 log_print("No queries found in file", "ERROR")
                 return jsonify({"error": "No queries found in file"}), 400
-        log_print(f"Received queries: {queries}")
+            log_print(f"Received queries: {queries}")
         except UnicodeDecodeError:
             log_print("Invalid file encoding", "ERROR")
             return jsonify({"error": "Invalid file encoding"}), 400
@@ -177,12 +181,12 @@ def compute_answers():
                 return jsonify({"error": "Missing answer CSV file"}), 400
                 
             try:
-            ans_csv_file = ans_csv_file.read().decode('utf-8').splitlines()
-            c_answers = []
-            for i in ans_csv_file:
-                c_answers.append(i.split('\\n'))
-            log_print(f"Processed CSV answers: {c_answers}")
-            return jsonify({"answers": c_answers}), 200
+                ans_csv_file = ans_csv_file.read().decode('utf-8').splitlines()
+                c_answers = []
+                for i in ans_csv_file:
+                    c_answers.append(i.split('\\n'))
+                log_print(f"Processed CSV answers: {c_answers}")
+                return jsonify({"answers": c_answers}), 200
             except UnicodeDecodeError:
                 log_print("Invalid CSV file encoding", "ERROR")
                 return jsonify({"error": "Invalid CSV file encoding"}), 400
@@ -253,14 +257,14 @@ def compute_marks():
             return jsonify({"error": "No answers provided"}), 400
         
         try:
-        log_print("=== Processing Answers ===")
-        log_print(f"Received answers: {a}")
-        a = json.loads(a)
-        answers = []
-        for i in a:
-            ans = i.split('\n\n')
-            answers.append(ans)
-        log_print(f"Processed answers structure: {answers}")
+            log_print("=== Processing Answers ===")
+            log_print(f"Received answers: {a}")
+            a = json.loads(a)
+            answers = []
+            for i in a:
+                ans = i.split('\n\n')
+                answers.append(ans)
+            log_print(f"Processed answers structure: {answers}")
         except json.JSONDecodeError:
             log_print("Invalid JSON format in answers", "ERROR")
             return jsonify({"error": "Invalid JSON format in answers"}), 400