changes with large file changes

Project/Backend/ML_Models/sign_language_to_text/.gitignore

+models/*
+!models/
\ No newline at end of file

Project/Backend/ML_Models/sign_language_to_text/.ipynb_checkpoints/tensorflow_sinhala-checkpoint.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Project/Backend/Server_Python/.gitignore

@@ -14,4 +14,182 @@
 *.ts
 
 files/*
-!files/
+!files/
\ No newline at end of file
+
+
+# Created by https://www.toptal.com/developers/gitignore/api/python
+# Edit at https://www.toptal.com/developers/gitignore?templates=python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+# End of https://www.toptal.com/developers/gitignore/api/python
\ No newline at end of file

Project/Backend/Server_Python/README.md

 # TMP-23-029
 
-SLIIT Final Year Project
+SLIIT Final Year Project
\ No newline at end of file
+
+Python Environment - Python 3.10.11
+
+Commands
+
+Install libraries
+pip install -r requirements.txt
+
+Run application
+uvicorn main:app --reload

Project/Backend/Server_Python/app.log

@@ -2,3 +2,5 @@
 2023-05-19 00:32:23,385 - INFO - Received request at root endpoint.
 2023-05-19 00:32:48,522 - ERROR - Received request at root endpoint.
 2023-05-19 00:32:48,522 - ERROR - Received request at root endpoint.
+2023-05-19 23:09:38,565 - INFO - Failed to make predictions. name 'CLASSES' is not defined
+2023-05-19 23:09:38,565 - INFO - Failed to make predictions. name 'CLASSES' is not defined

Project/Backend/Server_Python/controllers/translate_controler.py

@@ -7,6 +7,8 @@ from pydantic import BaseModel
 import tensorflow as tf
 
 from core import setup_logger
+from services.translate_service import SignLanguagePredictionService
+from utils import mappings
 
 
 router = APIRouter()
@@ -16,11 +18,15 @@ class ImageRequest(BaseModel):
     image: UploadFile
 
 # Load your Keras model
-model = tf.keras.models.load_model('D:\RP\SL-Detection-Action-Recognition\models\model')
-CLASSES = os.listdir('D:\RP\SL-Detection-Action-Recognition\data\Sn_sign_language_dataset')  # list of classes
-NUM_CLASSES = len(CLASSES)  # number of classes
+model = tf.keras.models.load_model('../ML_Models/sign_language_to_text/models/sign_language_model.h5')
+CLASSES = mappings.classes
+NUM_CLASSES = len(mappings.classes)  # number of classes
 IMG_SIZE = 224  # image size
 
+# Instantiate the service class
+prediction_service = SignLanguagePredictionService(model, CLASSES, mappings)
+
+
 @router.post("/upload/video")
 async def upload_video(video: UploadFile = File(...)):
     try:
@@ -37,27 +43,23 @@ async def upload_video(video: UploadFile = File(...)):
             detail="Failed to upload the video"
         )
     
-@router.post('/predict-sign-language')
-def predict(image_request:  UploadFile = File(...)):
+@router.post('/predict-sign-language/image')
+def predict_using_image(image_request: UploadFile = File(...)):
     try:
-        file_location = f"files/{image_request.filename}"
-        with open(file_location, "wb") as file:
-                file.write(image_request.file.read())
-
-        # Load the saved image using OpenCV
-        img = cv2.imread(file_location)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
-        img = np.array([img], dtype=np.float32) / 255.0
-
-        # Make prediction
-        prediction = model.predict(img)
-        class_name = CLASSES[np.argmax(prediction)]
-
-        return {'class_name': class_name}
+        return prediction_service.predict_sign_language(image_request)
+    except Exception as e:
+        logger.info(f"Error. {e}")
+        raise HTTPException(
+            status_code=500,
+            detail="Request Failed."
+        )
+@router.post('/predict-sign-language/video')
+def predict_using_video(video_request: UploadFile = File(...)):
+    try:
+        return prediction_service.predict_sign_language_video(video_request)
     except Exception as e:
-        logger.info(f"Failed to make predictions. {e}")
+        logger.info(f"Error. {e}")
         raise HTTPException(
             status_code=500,
-            detail="Failed to make predictions"
+            detail="Request Failed."
         )
\ No newline at end of file

Project/Backend/Server_Python/controllers/users_controller.py

@@ -2,6 +2,10 @@ from fastapi import APIRouter
 
 router = APIRouter()
 
+@router.get("/ping")
+def test():
+    # Your code here
+    return {"pong"}
 
 @router.get("/users")
 def get_users():

Project/Backend/Server_Python/services/translate_service.py

+import os
+import cv2
+import numpy as np
+
+from fastapi import HTTPException, UploadFile
+from typing import Dict
+
+import tensorflow as tf
+
+from core import setup_logger
+from utils import mappings
+
+logger = setup_logger()
+
+
+IMG_SIZE = 224  # image size
+
+class SignLanguagePredictionService:
+    def __init__(self, model, classes, mappings):
+        self.model = model
+        self.classes = classes
+        self.mappings = mappings
+
+    def predict_sign_language(self, image_request: UploadFile) -> Dict[str, str]:
+        try:
+            file_location = f"files/{image_request.filename}"
+            with open(file_location, "wb") as file:
+                file.write(image_request.file.read())
+
+            img = cv2.imread(file_location)
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+            img = np.array([img], dtype=np.float32) / 255.0
+
+            prediction = self.model.predict(img)
+            class_index = np.argmax(prediction)
+            class_name = self.classes[class_index]
+
+            sinhala_letter = self.mappings.letter_mapping.get(class_name, 'Unknown')
+
+            # Delete the image file
+            os.remove(file_location)
+
+            return {'prediction': sinhala_letter}
+        except Exception as e:
+            logger.info(f"Failed to make predictions. {e}")
+            raise HTTPException(
+                status_code=500,
+                detail="Failed to make predictions"
+            )
+        
+    def predict_sign_language_video(self, video_request: UploadFile) -> Dict[str, str]:
+        try:
+            # Create a temporary file to save the video
+            video_location = f"files/{video_request.filename}"
+            with open(video_location, "wb") as file:
+                file.write(video_request.file.read())
+
+            # Read the video using OpenCV
+            video = cv2.VideoCapture(video_location)
+
+            predictions = []
+            frame_count = 0
+
+            # Loop through the frames of the video
+            while frame_count < 20:
+                success, frame = video.read()
+                if not success:
+                    break
+
+                # Preprocess the frame
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frame = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
+                frame = np.array([frame], dtype=np.float32) / 255.0
+
+                # Make prediction
+                prediction = self.model.predict(frame)
+                class_index = np.argmax(prediction)
+                class_name = self.classes[class_index]
+
+                sinhala_letter = self.mappings.letter_mapping.get(class_name, 'Unknown')
+
+                # Store the prediction for the frame
+                predictions.append(sinhala_letter)
+                frame_count += 1
+
+            video.release()
+
+            # Delete the video file
+            os.remove(video_location)
+
+            return {'frame_count': frame_count, 'predictions': predictions}
+        except Exception as e:
+            logger.info(f"Failed to make predictions. {e}")
+            raise HTTPException(
+                status_code=500,
+                detail="Failed to make predictions"
+            )

Project/Backend/Server_Python/utils/mappings.py

+letter_mapping = {
+    'Ah': 'අ',
+    'Aah': 'ආ',
+    'Aeh': 'ඇ',
+    'Ee': 'ඉ',
+    'Eeh': 'ඊ',
+    'Uh': 'උ',
+    'Uhh': 'ඌ',
+    'A': 'එ',
+    'Ae': 'ඒ',
+    'O': 'ඔ',
+    'Ohh': 'ඕ',
+    'K': 'ක්',
+    'Ig': 'ග්',
+    'T': 'ටී'
+}
+classes =['A',
+ 'Aah',
+ 'Ae',
+ 'Aeh',
+ 'Ah',
+ 'Ee',
+ 'Eeh',
+ 'Ig',
+ 'K',
+ 'O',
+ 'Ohh',
+ 'T',
+ 'Uh',
+ 'Uhh']
\ No newline at end of file