Update app.py

app.py

@@ -1,64 +1,521 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+#"👑Ultron-Praim👑"
+import os
+import asyncio
+import logging
+from transformers import AutoTokenizer, TFAutoModel, pipeline
+from sentence_transformers import SentenceTransformer
+from rl.agents import PPOAgent, DQNAgent, SACAgent, MetaRLAgent
+from rl.memory import SequentialMemory
+import tensorflow as tf
+import numpy as np
+import torch
+import pandas as pd
+import shutil
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pandas_profiling import ProfileReport
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
+from ultralytics import YOLO
+from mlagents_envs.environment import UnityEnvironment
+from scipy import stats
+import feature_engine.creation as fe
+from PIL import Image
+import cv2
+import faiss
+from cryptography.fernet import Fernet
+import pyttsx3  # Text-to-Speech library
+import whisper  # Whisper library for STT
+import requests
+from bs4 import BeautifulSoup  # Web Scraping
+import networkx as nx  # Knowledge Graph Management
+import multiprocessing  # Multiprocessing for real-time task handling
+import qiskit  # Quantum Computing Library
 
+# Logging Configuration
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
+# Configuration
+CONFIG = {
+    "learning_rate": 1e-4,
+    "memory_limit": 10000,
+    "nb_actions": 5,
+    "tokenizer_model": "bert-base-uncased",
+    "sentence_embedder": "all-MiniLM-L6-v2",
+    "multimodal_model": "openai/clip-vit-base-patch32",
+    "index_path": "knowledge_index.faiss",
+    "whisper_model": "openai/whisper-base",
+    "t5_model": "t5-base",  # Few-Shot/Zero-Shot Learning
+    "automl_model": "h2o.ai/automl",  # AutoML Placeholder
+    "emotion_model": "microsoft/FacialEmotionRecognition",  # Emotion Detection Model
+    "yolo_model": "yolov3.cfg",  # YOLO Configuration
+    "yolo_weights": "yolov3.weights",  # YOLO Weights
+    "yolo_classes": "coco.names",  # YOLO Classes
+    "dalle_model": "dalle-mini/dalle-mini-1",  # Text-to-Image Model
+    "musenet_model": "muse-net/musenet-24000",  # Music Generation Model
+    "quantum_backend": "qiskit.basicAer",  # Quantum Computing Backend
+    "tts_model": "facebook/tts-en-transformer"  # Advanced Text-to-Speech
+}
+
+# Initialize Models
+tokenizer = AutoTokenizer.from_pretrained(CONFIG["tokenizer_model"])
+nlp_model = TFAutoModel.from_pretrained(CONFIG["tokenizer_model"])
+embedder = SentenceTransformer(CONFIG["sentence_embedder"])
+
+# Initialize Whisper for Speech-to-Text
+whisper_model = whisper.load_model(CONFIG["whisper_model"])
+
+# Initialize T5 for Few-Shot/Zero-Shot Learning
+t5_model = pipeline("text-generation", model=CONFIG["t5_model"])
+
+# Initialize Advanced TTS and VALL-E
+tts_model = pipeline("text-to-speech", model=CONFIG["tts_model"])
+
+# Memory Classes
+class ContextualMemory:
+    def __init__(self):
+        self.short_term_memory = []
+        self.long_term_memory = []
+
+    def add_to_memory(self, query, response, memory_type="short"):
+        memory = {"query": query, "response": response}
+        if memory_type == "short":
+            self.short_term_memory.append(memory)
+            if len(self.short_term_memory) > CONFIG["memory_limit"]:
+                self.short_term_memory.pop(0)
+        elif memory_type == "long":
+            self.long_term_memory.append(memory)
+
+    def retrieve_memory(self, memory_type="short"):
+        return self.short_term_memory if memory_type == "short" else self.long_term_memory
+
+
+
+# Security Module
+class SecurityHandler:
+    def __init__(self):
+        self.key = Fernet.generate_key()
+        self.cipher = Fernet(self.key)
+
+    def encrypt(self, data):
+        return self.cipher.encrypt(data.encode())
+
+    def decrypt(self, data):
+        return self.cipher.decrypt(data).decode()
+
+
+# Reinforcement Learning Agents
+class RLAgent:
+    def __init__(self, model_type="PPO"):
+        self.model_type = model_type
+        self.agent = self._initialize_agent()
+
+    def _initialize_agent(self):
+        if self.model_type == "PPO":
+            return PPOAgent()
+        elif self.model_type == "DQN":
+            return DQNAgent()
+        elif self.model_type == "SAC":
+            return SACAgent()
+        elif self.model_type == "MetaRL":
+            return MetaRLAgent()
+        else:
+            raise ValueError("Unsupported RL model type")
+
+    def act(self, state):
+        # Placeholder: Reinforcement learning decision-making
+        return f"Decision based on {self.model_type}: {state}"
+
+
+# Core AI System
+class Ultron:
+    def __init__(self):
+        self.context_memory = ContextualMemory()
+        self.multimodal_processor = MultimodalProcessor()
+        self.task_manager = TaskManager()
+        self.security = SecurityHandler()
+        self.rl_agents = {
+            "GandMaster": RLAgent(model_type="PPO"),
+            "MasterMind": RLAgent(model_type="PPO"),
+            "BrainA1": RLAgent(model_type="PPO"),
+            "BrainA2": RLAgent(model_type="DQN"),
+            "BrainA3": RLAgent(model_type="SAC"),
+            "BrainA4": RLAgent(model_type="HRL"),
+            "BrainA5": RLAgent(model_type="MetaRL"),
+        }
+        self.speaker = pyttsx3.init()  # Initialize text-to-speech engine
+        self.quantum_processor = QuantumProcessor()  # Initialize Quantum Processor
+        self.tts_model = tts_model  # Advanced Text-to-Speech Model
+
+    def speak(self, text):
+        """Converts text to speech."""
+        try:
+            self.speaker.say(text)
+            self.speaker.runAndWait()
+        except Exception as e:
+            logging.error(f"Text-to-Speech error: {e}")
+
+    async def process_query(self, query, input_type="text", file_path=None):
+        try:
+            if input_type == "text":
+                vectorized_query = tokenizer(query, return_tensors="tf", padding=True, truncation=True)
+                response = f"Processed text query: {query}"
+
+            elif input_type == "image":
+                response = self.multimodal_processor.process_image(file_path)
+
+            elif input_type == "video":
+                response = self.multimodal_processor.process_video(file_path)
+
+            elif input_type == "camera":
+                image_path = self.multimodal_processor.capture_image_from_camera()
+                response = self.multimodal_processor.process_image(image_path)
+
+            elif input_type == "speech":
+                result = whisper_model.transcribe(file_path)
+                response = result["text"]
+
+            elif input_type == "web":
+                response = self._web_scrape(query)
+
+            elif input_type == "emotion":
+                response = self._detect_emotion(file_path)
+
+            elif input_type == "yolo":
+                response = self.multimodal_processor.detect_objects(file_path)
+
+            elif input_type == "dalle":
+                response = self.generate_image(query)
+
+            elif input_type == "musenet":
+                response = self.generate_music(query)
+
+            elif input_type == "quantum":
+                circuit = qiskit.QuantumCircuit(2)
+                circuit.h(0)
+                circuit.cx(0, 1)
+                response = self.quantum_processor.run_quantum_circuit(circuit)
+
+            elif input_type == "tts":
+                response = self.tts_model(query)
+
+            else:
+                response = "Unsupported input type."
+
+            # Few-Shot/Zero-Shot Learning with T5
+            if input_type == "text":
+                if query.lower() not in [memory["query"].lower() for memory in self.context_memory.short_term_memory]:
+                    t5_response = t5_model(f"Translate this to a query: {query}")[0]["generated_text"]
+                    response += f" (Generated response: {t5_response})"
+
+            # Reinforcement Learning with Human Feedback
+            if input_type == "text":
+                feedback = input(f"Was the response helpful? (yes/no): ")
+                if feedback.lower() == "yes":
+                    decision = self.rl_agents["GandMaster"].act(response)
+                    self.context_memory.add_to_memory(query, response)
+                    self.speak(response)  # Speak function invoked for each response
+                    return f"{response} | RL Decision: {decision}"
+                elif feedback.lower() == "no":
+                    decision = self.rl_agents["GandMaster"].act("Incorrect response, seeking improvements.")
+                    self.context_memory.add_to_memory(query, "Incorrect response", memory_type="short")
+                    return "Sorry, let's try again with a better response."
+            return response
+
+        except Exception as e:
+            logging.error(f"Query processing error: {e}")
+            return "An error occurred while processing the query."
+
+    def _web_scrape(self, query):
+        try:
+            url = f"https://www.google.com/search?q={query.replace(' ', '+')}"
+            headers = {'User-Agent': 'Mozilla/5.0'}
+            page = requests.get(url, headers=headers)
+            soup = BeautifulSoup(page.content, 'html.parser')
+            result = soup.find('div', {'id': 'main'}).text.strip()
+            return result[:500]  # Limit results to avoid long responses
+        except Exception as e:
+            logging.error(f"Web scraping error: {e}")
+            return "Web scraping failed."
+
+
+# Task Management with Multiprocessing
+class TaskManager:
+    def __init__(self):
+        self.tasks = []
+
+    def add_task(self, task_name, priority=1):
+        self.tasks.append({"task": task_name, "priority": priority})
+        self.tasks = sorted(self.tasks, key=lambda x: x["priority"], reverse=True)
+
+    def get_next_task(self):
+        return self.tasks.pop(0) if self.tasks else None
+
+
+# Multimodal Processing
+class MultimodalProcessor:
+    def __init__(self):
+        self.clip_model = pipeline("feature-extraction", model=CONFIG["multimodal_model"])
+        self.net = cv2.dnn.readNetFromDarknet(CONFIG["yolo_model"], CONFIG["yolo_weights"])
+        self.net.setInput(cv2.dnn.blobFromImage)
+
+    def process_image(self, image_path):
+        try:
+            image = Image.open(image_path)
+            features = self.clip_model(image)
+            return features
+        except Exception as e:
+            logging.error(f"Image processing error: {e}")
+            return None
+
+    def process_video(self, video_path):
+        try:
+            video_frames = self._extract_video_frames(video_path)
+            features = [self.clip_model(frame) for frame in video_frames]
+            return features
+        except Exception as e:
+            logging.error(f"Video processing error: {e}")
+            return None
+
+    def _extract_video_frames(self, video_path, frame_rate=8):
+        cap = cv2.VideoCapture(video_path)
+        frames = []
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frames.append(frame)
+        cap.release()
+        return frames[::frame_rate]
+
+    def capture_image_from_camera(self):
+        cap = cv2.VideoCapture(0)
+        ret, frame = cap.read()
+        image_path = "camera_capture.jpg"
+        cv2.imwrite(image_path, frame)
+        cap.release()
+        return image_path
+
+    def detect_objects(self, image_path):
+        try:
+            image = cv2.imread(image_path)
+            height, width = image.shape[:2]
+            self.net.setInput(cv2.dnn.blobFromImage(image, scalefactor=1/255, size=(416, 416), swapRB=True, crop=False))
+            outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+            for detection in outs[0]:
+                confidence = detection[5:]
+                class_id = np.argmax(confidence)
+                confidence_score = confidence[class_id]
+                if confidence_score > 0.5:  # Confidence threshold
+                    box = detection[:4] * np.array([width, height, width, height])
+                    center_x, center_y, box_width, box_height = box.astype(int)
+                    start_x, start_y = int(center_x - box_width / 2), int(center_y - box_height / 2)
+                    end_x, end_y = int(center_x + box_width / 2), int(center_y + box_height / 2)
+                    cv2.rectangle(image, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
+                    cv2.putText(image, f"{class_id} {confidence_score:.2f}", (start_x, start_y - 10),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+            cv2.imwrite("object_detected.jpg", image)
+            return "object_detected.jpg"
+        except Exception as e:
+            logging.error(f"Object detection error: {e}")
+            return None
+        
+    def _detect_emotion(self, image_path):
+        try:
+            image = Image.open(image_path)
+            features = self.multimodal_processor.clip_model(image)
+            emotion = features[0][0]
+            return f"Detected Emotion: {emotion}"
+        except Exception as e:
+            logging.error(f"Emotion detection error: {e}")
+            return "Emotion detection failed."
+
+    def data_analysis():
+
+        # Function to load data
+        def load_data():
+            file_path = input("Enter the dataset path: ").strip()
+            try:
+                if file_path.endswith('.csv'):
+                    data = pd.read_csv(file_path)
+                elif file_path.endswith('.xlsx'):
+                    data = pd.read_excel(file_path)
+                elif file_path.endswith('.json'):
+                    data = pd.read_json(file_path)
+                else:
+                    raise ValueError("Unsupported file format. Use CSV, Excel, or JSON.")
+                print("Data loaded successfully!")
+                return data
+            except Exception as e:
+                print(f"Error loading data: {e}")
+                return None
+
+        # Function to clean data
+        def clean_data(data):
+            print("\nCleaning data...")
+            data.fillna(data.mean(), inplace=True)
+            data.drop_duplicates(inplace=True)
+            for col in data.select_dtypes(include=np.number):
+                z_scores = np.abs(stats.zscore(data[col]))
+                data = data[(z_scores < 3)]
+            print("Data cleaning completed!")
+            return data
+
+        # Function for exploratory data analysis
+        def perform_eda(data):
+            print("\nPerforming EDA...")
+            profile = ProfileReport(data, title="EDA Report", explorative=True)
+            profile.to_file("eda_report.html")
+            sns.heatmap(data.corr(), annot=True, cmap="coolwarm")
+            plt.title("Correlation Matrix")
+            plt.show()
+            print("EDA report saved as 'eda_report.html'.")
+
+        # Function for feature engineering
+        def feature_engineering(data):
+            print("\nPerforming feature engineering...")
+            if 'time' in data.columns:
+                transformer = fe.CyclicFeatures(variables=['time'], max_value=24)
+                data = transformer.fit_transform(data)
+                print("Cyclic features created!")
+            else:
+                print("'time' column not found. Skipping cyclic features.")
+            return data
+
+        # Function to build and train a combined deep learning model with pre-trained layers
+        def build_and_train_dnn(data):
+            print("\nBuilding and training combined deep learning model...")
+            target = data.columns[-1]  # Assume last column is the target
+            features = data.drop(columns=[target])
+            labels = data[target]
+
+            # Encode labels if categorical
+            if labels.dtypes == 'object':
+                labels = LabelEncoder().fit_transform(labels)
+
+            # Split the data
+            X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
+            scaler = StandardScaler()
+            X_train = scaler.fit_transform(X_train)
+            X_test = scaler.transform(X_test)
+
+            # Combined model architecture
+            model = Sequential([
+                Dense(128, activation='relu', input_dim=X_train.shape[1]),
+                BatchNormalization(),
+                Dropout(0.3),
+                Dense(64, activation='relu'),
+                BatchNormalization(),
+                Dense(32, activation='relu'),
+                Dropout(0.2),
+                Dense(1, activation='sigmoid')  # For binary classification
+            ])
+            model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+            model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)
+
+            # Evaluate the model
+            y_pred = (model.predict(X_test) > 0.5).astype("int32")
+            print("\nModel Performance:")
+            print(classification_report(y_test, y_pred))
+            return model
+
+        # Function for YOLO object detection
+        def yolo_object_detection(source="input_video.mp4"):
+            print("\nPerforming object detection...")
+            yolo_model = YOLO('yolov8n.pt')  # Pre-trained YOLO model
+            yolo_model.predict(source=source, save=True)
+            print("YOLO object detection completed. Results saved!")
+
+        # Function for Unity ML-Agents integration
+        def unity_integration(unity_env_path):
+            print("\nIntegrating Unity ML-Agents...")
+            if os.path.exists(unity_env_path):
+                unity_env = UnityEnvironment(file_name=unity_env_path)
+                unity_env.reset()
+                print("Unity environment loaded!")
+            else:
+                print("Unity environment not found. Skipping Unity integration.")
+
+        # Function to save outputs
+        def save_outputs(data):
+            os.makedirs("outputs", exist_ok=True)
+            data.to_csv("outputs/cleaned_data.csv", index=False)
+            shutil.move("eda_report.html", "outputs/eda_report.html")
+            print("All outputs saved in 'outputs' folder!")
+
+        # Main function to execute the workflow
+        def main():
+            try:
+                # Step 1: Load Data
+                data = load_data()
+                if data is None:
+                    return
+
+                # Step 2: Clean Data
+                data = clean_data(data)
+
+                # Step 3: EDA
+                perform_eda(data)
+
+                # Step 4: Feature Engineering
+                data = feature_engineering(data)
+
+                # Step 5: Train Combined Deep Learning Model
+                model = build_and_train_dnn(data)
+
+                # Step 6: YOLO Object Detection
+                yolo_object_detection()
+
+                # Step 7: Unity ML-Agents Integration
+                unity_env_path = input("\nEnter Unity environment path (optional): ").strip()
+                if unity_env_path:
+                    unity_integration(unity_env_path)
+
+                # Step 8: Save Outputs
+                save_outputs(data)
+
+                print("\nAll tasks completed successfully!")
+            except Exception as e:
+                print(f"An error occurred: {e}")
+
+        # Entry point
+        #if __name__ == "__main__":
+        #    main()
+
+    def generate_image(self, text):
+        try:
+            dalle_model = pipeline("text-to-image", model=CONFIG["dalle_model"])
+            generated_image = dalle_model(text)[0]["generated_image"]
+            return generated_image
+        except Exception as e:
+            logging.error(f"Image generation error: {e}")
+            return "Image generation failed."
+
+    def generate_music(self, prompt):
+        try:
+            musenet_model = pipeline("music-generation", model=CONFIG["musenet_model"])
+            generated_music = musenet_model(prompt)[0]["generated_music"]
+            return generated_music
+        except Exception as e:
+            logging.error(f"Music generation error: {e}")
+            return "Music generation failed."
+
+    async def run(self):
+        logging.info("Ultron started.")
+        while True:
+            user_input = input("Enter your query (or type 'exit'): ")
+            if user_input.lower() in ["exit", "quit"]:
+                logging.info("Shutting down Ultron. Goodbye!")
+                break
+
+            response = await self.process_query(user_input)
+            print(f"Ultron Response: {response}")
+
+
+# Main Execution
 if __name__ == "__main__":
-    demo.launch()
+    ultron = Ultron()
+    asyncio.run(ultron.run())