SAM is an intelligent robot designed to enhance daily life with its remarkable capabilities. Powered by OpenAI's Davinci-002 engine, SAM combines conversational intelligence, facial recognition, and versatile functionalities to create a personalized, engaging, and helpful experience.

- Recognizes and interacts with individuals through advanced facial recognition.

- Greets individuals with a handshake, offering a personalized and engaging experience.

- Equipped with natural language processing to engage in lifelike conversations.

- Provides detailed and accurate responses on a variety of topics.

- Utilizes OpenAI's API for dynamic, real-time conversational capabilities.

- **Security Bot**:

- Monitors homes or events for enhanced security.

- Assists with real-time information and alerting.

- **Hospitality**:

- Enhances guest experiences in hotels and restaurants.

- Assists staff by answering guest inquiries and providing directions.

- **Information Assistance**:

- Functions as an information kiosk in public spaces.

- Provides guidance and support to visitors efficiently.

- **Programming Languages**: Python

- **Libraries**:

- `face_recognition`: For advanced facial recognition.

- `cv2 (OpenCV)`: For real-time video feed and facial interaction.

- `pyttsx3`: For voice synthesis.

- `speech_recognition`: For voice command input.

- `openai`: To power conversational abilities.

- **`ccv.py`**: Real-time facial recognition script.

- **`frec.py`**: Haar Cascade-based face detection script for efficient face tracking.

- **`sam.py`**: Main script implementing SAM’s conversational engine, facial recognition, and versatile functionalities.

1. **Setup**:

- Install the required libraries:

```bash

pip install -r requirements.txt

```

- Place images of recognizable faces in the project directory and ensure proper naming for mapping.

2. **Run SAM**:

- Start SAM’s core functionality by running `sam.py`:

```bash

3. **Interact**:

- Use voice commands or direct inputs for interaction.

- SAM will respond, perform tasks, or provide assistance based on the given command.

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SAM is not just a robot; it’s a companion and assistant. Its extensible design allows integration into various environments, from homes and offices to public spaces. Potential improvements include:

- **Enhanced Mobility**: Integrating robotic arms or wheels for physical task execution.

- **IoT Integration**: Connecting with smart home devices for better control and automation.

- **Custom Personality Profiles**: Allowing users to tailor SAM’s interaction style to their preferences.

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- **Yashwanth K**@Yashwanss **Jeevan Suresh**

SAM is your friendly assistant, redefining what it means to have a truly intelligent machine in your life. Try it today and experience the future of robotics!

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This project is a labor of passion and curiosity, and while every effort has been made to ensure the code is functional, there may still be imperfections. I apologize for any rough edges or inefficiencies in the code and would deeply appreciate your feedback and improvements.

---

import numpy as np

import face_recognition as fr

import cv2

import time

video_capture = cv2.VideoCapture(0)

Jvn_image = fr.load_image_file("Me.jpg") # Rename Your Detected Face Photo as Me.jpg And Keep Both Python File And This File In The Same Script

Jvn_face_encoding = fr.face_encodings(Jvn_image)[0]

known_face_encondings = [Jvn_face_encoding]

known_face_names = ["Jeevan"] # Change It To Your Name

while True:

ret, frame = video_capture.read()

frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)

rgb_frame = frame[:, :, ::-1]

face_locations = fr.face_locations(rgb_frame)

face_encodings = fr.face_encodings(rgb_frame, face_locations)

face_detected = False

for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

matches = fr.compare_faces(known_face_encondings, face_encoding)

name = "Unable To Recognize"

face_distances = fr.face_distance(known_face_encondings, face_encoding)

best_match_index = np.argmin(face_distances)

if matches[best_match_index]:

name = known_face_names[best_match_index]

face_detected = True

cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

font = cv2.FONT_HERSHEY_SIMPLEX

cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

cv2.imshow('Face_Recognition', frame)

k = cv2.waitKey(1) & 0xFF

if k == 27:

break

video_capture.release()

cv2.destroyAllWindows()