Merge pull request #26 from OneFineStarstuff/penify/auto_doc_ea03621_86c59

[Penify]: Documentation for commit - ea03621

Author: OneFineStarstuff

agi-pipeline.py

@@ -58,9 +58,7 @@ def __init__(self, model_name="facebook/bart-large-cnn"):
         )  # nosec B615
 
     def process_text(self, text, max_length=25, num_beams=5):
-        """
-        Method process_text.
-        """
+        """Process and summarize the given text using a model."""
         logging.info("Processing text for summarization")
         try:
             inputs = self.tokenizer(
@@ -119,9 +117,7 @@ def preprocess_large_image(image_path, max_size=(2000, 2000)):
             return None
 
     def process_image(self, image_path):
-        """
-        Method process_image.
-        """
+        """Process an image for classification."""
         logging.info("Processing image for classification")
         try:
             image_path = self.preprocess_large_image(
@@ -156,9 +152,7 @@ def __init__(self):
         )
 
     def process_image(self, image_path):
-        """
-        Method process_image.
-        """
+        """Process an image for classification with augmentation."""
         logging.info("Processing image with augmentation for classification")
         try:
             image_path = self.preprocess_large_image(
@@ -227,16 +221,12 @@ def __init__(self):
         self.state = 50
 
     def reset(self):
-        """
-        Method reset.
-        """
+        """Resets the state to 50 and returns it as a numpy array."""
         self.state = 50
         return np.array([self.state], dtype=np.float32)
 
     def step(self, action):
-        """
-        Method step.
-        """
+        """Executes a step in the environment based on the given action."""
         reward = -abs(self.state - (50 + action * 10))
         self.state += action - 2
         done = self.state <= 0 or self.state >= 100
@@ -256,9 +246,7 @@ def __init__(self):
         self.model = PPO("MlpPolicy", self.env, verbose=1)
 
     def train(self, timesteps=10000):
-        """
-        Method train.
-        """
+        """Trains the RL model for a specified number of timesteps."""
         logging.info("Training RL model")
         try:
             self.model.learn(total_timesteps=timesteps)
@@ -267,9 +255,7 @@ def train(self, timesteps=10000):
             logging.error(f"Error in RLModule training: {e}")
 
     def save_model(self, path):
-        """
-        Method save_model.
-        """
+        """Saves the model to the specified path."""
         try:
             self.model.save(path)
             logging.info(f"Model saved to {path}")
@@ -341,9 +327,7 @@ def extract_frames(
         return frame_count
 
     def process_frame(self, frame_path):
-        """
-        Method process_frame.
-        """
+        """Processes an image frame and returns a tensor."""
         try:
             image = Image.open(frame_path).convert("RGB")
             tensor = self.transform(image).unsqueeze(0)
@@ -365,8 +349,17 @@ def __init__(self):
         super().__init__()
 
     def process_real_time_video(self, source=0):
-        """
-        Method process_real_time_video.
+        """Process real-time video from a specified source.
+        
+        This method captures video from the given source and processes each frame  in
+        real-time. It checks if the video source is opened successfully, and if  not,
+        logs an error. The frames are resized and transformed before being  displayed
+        in a window. The processing continues until the video ends or  the user presses
+        the 'q' key to quit. Finally, it releases the video  capture and closes all
+        OpenCV windows.
+        
+        Args:
+            source (int or str): The video source, which can be an integer for
         """
         cap = cv2.VideoCapture(source)
         if not cap.isOpened():
@@ -401,9 +394,7 @@ def __init__(self):
         self.engine = pyttsx3.init()
 
     def speech_to_text(self, audio_file):
-        """
-        Method speech_to_text.
-        """
+        """Converts speech from an audio file to text."""
         try:
             with sr.AudioFile(audio_file) as source:
                 audio = self.recognizer.record(source)
@@ -443,9 +434,7 @@ def __init__(self):
         self.voice_processor = VoiceProcessor()
 
     def process_input(self, text=None, image_path=None):
-        """
-        Method process_input.
-        """
+        """Processes text and image input and returns the results."""
         results = {}
         if text:
             results["nlp"] = self.nlp.process_text(text)
@@ -454,25 +443,19 @@ def process_input(self, text=None, image_path=None):
         return results
 
     def process_multi_modal(self, text, image_path):
-        """
-        Method process_multi_modal.
-        """
+        """Processes text and image using multi-modal processing."""
         return self.multi_modal.process_text_image(text, image_path)
 
     def process_video(self, video_path, frame_output_dir):
-        """
-        Method process_video.
-        """
+        """Process a video and extract its frames."""
         frame_count = self.video_processor.extract_frames(video_path, frame_output_dir)
         if frame_count == 0:
             logging.error("No frames were saved. Please check the video file and path.")
             return
         logging.info(f"Video frames processed and saved to {frame_output_dir}")
 
     def process_real_time_video(self, source=0):
-        """
-        Method process_real_time_video.
-        """
+        """Processes real-time video from the specified source."""
         self.real_time_video_processor.process_real_time_video(source)
 
     def train_rl(self, timesteps=10000):
@@ -482,15 +465,11 @@ def train_rl(self, timesteps=10000):
         self.rl.train(timesteps)
 
     def choose_action(self, state):
-        """
-        Method choose_action.
-        """
+        """Selects an action based on the given state."""
         return self.rl.choose_action(state)
 
     def visualize_data(self, data):
-        """
-        Method visualize_data.
-        """
+        """Visualizes the given data using a bar chart."""
         try:
             fig = px.bar(
                 x=list(data.keys()), y=list(data.values()), title="Data Visualization"
@@ -500,15 +479,11 @@ def visualize_data(self, data):
             logging.error(f"Error in data visualization: {e}")
 
     def speech_to_text(self, audio_file):
-        """
-        Method speech_to_text.
-        """
+        """Converts speech from an audio file to text."""
         return self.voice_processor.speech_to_text(audio_file)
 
     def text_to_speech(self, text):
-        """
-        Method text_to_speech.
-        """
+        """Converts text to speech using the voice processor."""
         self.voice_processor.text_to_speech(text)
 
 

cv_module.py

@@ -22,15 +22,14 @@ def __init__(self):
         logger.info("CV model loaded successfully.")
 
     def detect_objects(self, image: Image.Image) -> str:
-        """
-        Detects objects in the provided image.
-
+        """Detects objects in the provided image.
+        
         Args:
             image (Image.Image): The input image.
-
+        
         Returns:
             str: JSON string containing detection results.
-
+        
         Raises:
             ValueError: If the image is None.
         """

fix_agi.py

@@ -3,6 +3,7 @@
 
 def add_docstrings(content):
     # Add module docstring if missing
+    """Add missing module and class/method/function docstrings to the content."""
     if not content.startswith('"""'):
         content = '"""\nAGI Pipeline Legacy Module.\n"""\n' + content
 

main.py

@@ -33,21 +33,15 @@ def __init__(self):
         self.speech = SpeechProcessor()
 
     def process_nlp(self, prompt: str) -> str:
-        """
-        Processes text using the NLP module.
-        """
+        """Processes text using the NLP module."""
         return self.nlp.generate_text(prompt)
 
     def process_cv(self, image: Image.Image) -> str:
-        """
-        Processes an image using the CV module.
-        """
+        """Processes an image to detect objects using the CV module."""
         return self.cv.detect_objects(image)
 
     def process_stt(self, file: UploadFile) -> str:
-        """
-        Processes audio using the STT module.
-        """
+        """Processes audio and converts it to text using the STT module."""
         return self.speech.speech_to_text(file)
 
     def process_tts(self, text: str) -> None:
@@ -62,18 +56,14 @@ def process_tts(self, text: str) -> None:
 
 
 def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
-    """
-    Verifies the Bearer token in the Authorization header.
-    """
+    """Verifies the Bearer token in the Authorization header."""
     if credentials.credentials != VALID_API_KEY:
         raise HTTPException(status_code=403, detail="Forbidden")
 
 
 @app.post("/process-nlp/", dependencies=[Depends(verify_token)])
 async def process_nlp(data: dict):
-    """
-    Endpoint for text generation.
-    """
+    """Processes natural language input and generates a response."""
     try:
         prompt = data.get("text", "")
         return {"response": agi.process_nlp(prompt)}
@@ -84,9 +74,7 @@ async def process_nlp(data: dict):
 
 @app.post("/process-cv-detection/", dependencies=[Depends(verify_token)])
 async def process_cv_detection(file: UploadFile = File(...)):
-    """
-    Endpoint for object detection in images.
-    """
+    """Handles object detection in uploaded images."""
     try:
         image_data = await file.read()
         image = Image.open(BytesIO(image_data))
@@ -98,9 +86,7 @@ async def process_cv_detection(file: UploadFile = File(...)):
 
 @app.post("/speech-to-text/", dependencies=[Depends(verify_token)])
 async def speech_to_text(file: UploadFile = File(...)):
-    """
-    Endpoint for Speech-to-Text conversion.
-    """
+    """Converts speech from an uploaded file to text."""
     try:
         return {"response": agi.process_stt(file)}
     except Exception as e:
@@ -110,9 +96,7 @@ async def speech_to_text(file: UploadFile = File(...)):
 
 @app.post("/text-to-speech/", dependencies=[Depends(verify_token)])
 async def text_to_speech(data: dict):
-    """
-    Endpoint for Text-to-Speech conversion.
-    """
+    """Converts text to speech."""
     try:
         text = data.get("text", "")
         agi.process_tts(text)

nlp_module.py

@@ -27,15 +27,11 @@ def __init__(self):
         logger.info("NLP model loaded successfully.")
 
     def generate_text(self, prompt: str) -> str:
-        """
-        Generates text based on the provided prompt.
-
+        """Generates text based on the provided prompt.
+        
         Args:
             prompt (str): The input text to process.
-
-        Returns:
-            str: The generated response.
-
+        
         Raises:
             ValueError: If the prompt is empty.
         """