tmp.py

from google.colab import drive

# Mount Google Drive
drive.mount("/content/drive")

import logging
import os

import albumentations as A
import cv2
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
import pyttsx3
import seaborn as sns
import speech_recognition as sr
import torch
import uvicorn
from celery import Celery
from fastapi import Depends, FastAPI, File, UploadFile
from fastapi.security import OAuth2PasswordBearer
from gym import Env
from gym.spaces import Box, Discrete
from PIL import Image
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from torchvision import models, transforms
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, CLIPModel, CLIPProcessor

# Hugging Face Authentication (Optional)
HF_TOKEN = os.environ.get("HF_TOKEN", None)

# Setting up logging
logging.basicConfig(level=logging.INFO)

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")


class NLPModule:
    def __init__(self, model_name="facebook/bart-large-cnn"):
        self.tokenizer = AutoTokenizer.from_pretrained(
            model_name, use_auth_token=HF_TOKEN
        )  # nosec B615
        self.model = AutoModelForSeq2SeqLM.from_pretrained(
            model_name, use_auth_token=HF_TOKEN
        )  # nosec B615

    def process_text(self, text, max_length=25, num_beams=5):
        logging.info("Processing text for summarization")
        try:
            inputs = self.tokenizer(
                text, return_tensors="pt", max_length=512, truncation=True
            )
            outputs = self.model.generate(
                inputs["input_ids"],
                max_length=max_length,
                min_length=10,
                num_beams=num_beams,
            )
            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        except Exception as e:
            logging.error(f"Error in NLPModule: {e}")
            return "NLP processing error"


class CVModule:
    def __init__(self):
        self.model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
        self.model.eval()
        self.transform = transforms.Compose(
            [
                transforms.Resize((224, 224)),
                transforms.RandomHorizontalFlip(),
                transforms.ColorJitter(
                    brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5
                ),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
            ]
        )

    @staticmethod
    def preprocess_large_image(image_path, max_size=(2000, 2000)):
        try:
            with Image.open(image_path) as img:
                img.thumbnail(max_size)
                resized_path = "resized_image.jpg"
                img.save(resized_path)
            return resized_path
        except Exception as e:
            logging.error(f"Error in preprocessing image: {e}")
            return None

    def process_image(self, image_path):
        logging.info("Processing image for classification")
        try:
            image_path = self.preprocess_large_image(
                image_path
            )  # Ensure the image is manageable
            image = Image.open(image_path).convert("RGB")
            tensor = self.transform(image).unsqueeze(0)
            with torch.no_grad():
                outputs = self.model(tensor)
            return outputs.argmax().item()
        except Exception as e:
            logging.error(f"Error in CVModule: {e}")
            return "CV processing error"


class AdvancedDataAugmentation(CVModule):
    def __init__(self):
        super().__init__()
        self.aug = A.Compose(
            [
                A.HorizontalFlip(p=0.5),
                A.RandomBrightnessContrast(p=0.5),
                A.Rotate(limit=40, p=0.5),
            ]
        )

    def process_image(self, image_path):
        logging.info("Processing image with augmentation for classification")
        try:
            image_path = self.preprocess_large_image(
                image_path
            )  # Ensure the image is manageable
            image = Image.open(image_path).convert("RGB")
            image = np.array(image)
            augmented = self.aug(image=image)
            image = augmented["image"]
            tensor = self.transform(image).unsqueeze(0)
            with torch.no_grad():
                outputs = self.model(tensor)
            return outputs.argmax().item()
        except Exception as e:
            logging.error(f"Error in AdvancedDataAugmentation: {e}")
            return "CV processing error"


class MultiModalModule:
    def __init__(self, model_name="openai/clip-vit-base-patch32"):
        self.processor = CLIPProcessor.from_pretrained(
            model_name, use_auth_token=HF_TOKEN
        )  # nosec B615
        self.model = CLIPModel.from_pretrained(
            model_name, use_auth_token=HF_TOKEN
        )  # nosec B615

    def process_text_image(self, text, image_path):
        logging.info("Processing text and image for multi-modal integration")
        try:
            image_path = CVModule.preprocess_large_image(image_path)
            image = Image.open(image_path)
            inputs = self.processor(
                text=[text], images=[image], return_tensors="pt", padding=True
            )
            outputs = self.model(**inputs)
            logits_per_image = outputs.logits_per_image
            return logits_per_image.softmax(dim=1)
        except Exception as e:
            logging.error(f"Error in MultiModalModule: {e}")
            return "Multi-modal processing error"


class CustomEnv(Env):
    def __init__(self):
        super().__init__()
        self.action_space = Discrete(5)
        self.observation_space = Box(low=0, high=100, shape=(1,), dtype=np.float32)
        self.state = 50

    def reset(self):
        self.state = 50
        return np.array([self.state], dtype=np.float32)

    def step(self, action):
        reward = -abs(self.state - (50 + action * 10))
        self.state += action - 2
        done = self.state <= 0 or self.state >= 100
        return np.array([self.state], dtype=np.float32), reward, done, {}


class RLModule:
    def __init__(self):
        self.env = DummyVecEnv([lambda: CustomEnv()])
        self.model = PPO("MlpPolicy", self.env, verbose=1)

    def train(self, timesteps=10000):
        logging.info("Training RL model")
        try:
            self.model.learn(total_timesteps=timesteps)
            self.save_model("ppo_custom_env")
        except Exception as e:
            logging.error(f"Error in RLModule training: {e}")

    def save_model(self, path):
        try:
            self.model.save(path)
            logging.info(f"Model saved to {path}")
        except Exception as e:
            logging.error(f"Error saving RL model: {e}")

    def load_model(self, path):
        try:
            self.model = PPO.load(path, env=self.env)
            logging.info(f"Model loaded from {path}")
        except Exception as e:
            logging.error(f"Error loading RL model: {e}")

    def choose_action(self, state):
        try:
            action, _ = self.model.predict(state)
            return action
        except Exception as e:
            logging.error(f"Error predicting action: {e}")
            return "RL action error"


class VideoProcessor:
    def __init__(self):
        self.transform = transforms.Compose(
            [
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
            ]
        )

    def extract_frames(
        self, video_path, output_dir, frame_interval=30
    ):  # Adjust frame_interval to save fewer frames
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            logging.error(f"Unable to open video file: {video_path}")
            return 0
        frame_count = 0
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            if frame_count % frame_interval == 0:
                frame_path = os.path.join(output_dir, f"frame_{frame_count:04d}.jpg")
                cv2.imwrite(frame_path, frame)
                logging.info(f"Frame saved: {frame_path}")
            frame_count += 1
        cap.release()
        logging.info(f"Extracted {frame_count} frames from {video_path}")
        return frame_count

    def process_frame(self, frame_path):
        try:
            image = Image.open(frame_path).convert("RGB")
            tensor = self.transform(image).unsqueeze(0)
            return tensor
        except Exception as e:
            logging.error(f"Error processing frame: {e}")
            return "Frame processing error"


class RealTimeVideoProcessor(VideoProcessor):
    def __init__(self):
        super().__init__()

    def process_real_time_video(self, source=0):
        cap = cv2.VideoCapture(source)
        if not cap.isOpened():
            logging.error(f"Unable to open video source: {source}")
            return
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            # Process frame
            frame = cv2.resize(frame, (224, 224))
            tensor = self.transform(frame).unsqueeze(0)
            # Example of real-time processing
            cv2.imshow("Real-Time Video Processing", frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        cap.release()
        cv2.destroyAllWindows()
        logging.info("Real-time video processing completed")


class VoiceProcessor:
    def __init__(self):
        self.recognizer = sr.Recognizer()
        self.engine = pyttsx3.init()

    def speech_to_text(self, audio_file):
        try:
            with sr.AudioFile(audio_file) as source:
                audio = self.recognizer.record(source)
                text = self.recognizer.recognize_google(audio)
                return text
        except Exception as e:
            logging.error(f"Error in speech to text: {e}")
            return "Speech to text error"

    def text_to_speech(self, text):
        try:
            self.engine.say(text)
            self.engine.runAndWait()
        except Exception as e:
            logging.error(f"Error in text to speech: {e}")


class EnhancedAGIPipeline:
    def __init__(self):
        self.nlp = NLPModule()
        self.cv = CVModule()
        self.rl = RLModule()
        self.multi_modal = MultiModalModule()
        self.video_processor = VideoProcessor()
        self.real_time_video_processor = RealTimeVideoProcessor()
        self.augmented_cv = AdvancedDataAugmentation()
        self.voice_processor = VoiceProcessor()

    def process_input(self, text=None, image_path=None):
        results = {}
        if text:
            results["nlp"] = self.nlp.process_text(text)
        if image_path:
            results["cv"] = self.cv.process_image(image_path)
        return results

    def process_multi_modal(self, text, image_path):
        return self.multi_modal.process_text_image(text, image_path)

    def process_video(self, video_path, frame_output_dir):
        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):
        self.real_time_video_processor.process_real_time_video(source)

    def train_rl(self, timesteps=10000):
        self.rl.train(timesteps)

    def choose_action(self, state):
        return self.rl.choose_action(state)

    def visualize_data(self, data):
        try:
            fig = px.bar(
                x=list(data.keys()), y=list(data.values()), title="Data Visualization"
            )
            fig.show()
        except Exception as e:
            logging.error(f"Error in data visualization: {e}")

    def speech_to_text(self, audio_file):
        return self.voice_processor.speech_to_text(audio_file)

    def text_to_speech(self, text):
        self.voice_processor.text_to_speech(text)


# FastAPI Integration
agi = EnhancedAGIPipeline()
app = FastAPI()


@app.post("/process/")
async def process_pipeline(text: str, video: UploadFile):
    video_path = f"/content/{video.filename}"
    with open(video_path, "wb") as f:
        f.write(await video.read())
    result = agi.process_multi_modal(text, video_path)
    return result


@app.post("/nlp/")
async def process_nlp(text: str):
    result = agi.process_input(text=text)
    return {"summary": result["nlp"]}


@app.post("/cv/")
async def process_cv(image: UploadFile):
    image_path = f"/content/{image.filename}"
    with open(image_path, "wb") as f:
        f.write(await image.read())
    result = agi.process_input(image_path=image_path)
    return {"classification": result["cv"]}


@app.post("/real-time-video/")
async def process_real_time_video():
    agi.process_real_time_video(source=0)
    return {"message": "Real-time video processing started"}


@app.post("/speech-to-text/")
async def speech_to_text(audio: UploadFile):
    audio_path = f"/content/{audio.filename}"
    with open(audio_path, "wb") as f:
        f.write(await audio.read())
    text = agi.speech_to_text(audio_path)
    return {"text": text}


@app.post("/text-to-speech/")
async def text_to_speech(text: str):
    agi.text_to_speech(text)
    return {"message": "Text to speech conversion completed"}


@app.get("/secure-endpoint/")
async def read_secure_data(token: str = Depends(oauth2_scheme)):
    return {"message": "Secure data"}


if __name__ == "__main__":
    import nest_asyncio

    nest_asyncio.apply()
    uvicorn.run(app, host="127.0.0.1", port=8000)