# Chinnu AI with Quantum Integration
# Gift for my best friend
Chinnu AI is a quantum-inspired chatbot framework that merges traditional deep learning methods with quantum computing concepts to deliver advanced conversational experiences.
## Features
- **Quantum State Representation**: Utilize `QuantumState` for quantum-inspired computations.
- **Training Framework**: Train quantum-enhanced neural networks using `QuantumTrainer`.
- **Dynamic Neural Network**: Leverage PyTorch-based deep learning for conversational AI.
- **JSON-based Conversations**: Enable flexible interaction using structured JSON inputs.
- **Modular Design**: Easily extend or integrate into existing systems.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/chinnu-ai.git
cd chinnu-ai
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Install the package:
```bash
pip install .
```
## Usage
### Training Chinnu AI
Here is an example to train Chinnu AI with quantum integration:
```python
import torch
from ChinnuAi import QuantumState, QuantumTrainer, initialize_chinnu_ai, train_chinnu_ai
# Initialize Quantum State and Trainer
initial_state = [1, 0, 0, 0]
target_state = [0.5, 0.5, 0.5, 0.5]
quantum_model = QuantumState(initial_state)
quantum_trainer = QuantumTrainer(quantum_model, training_data=None)
# Initialize Neural Network
input_size = 4
hidden_size = 8
output_size = 4
model = initialize_chinnu_ai(input_size, hidden_size, output_size)
# Example Dataset
dataset = [
(torch.tensor([1, 0, 0, 0]), torch.tensor([0.5, 0.5, 0.5, 0.5])),
(torch.tensor([0, 1, 0, 0]), torch.tensor([0.5, 0.5, 0.5, 0.5])),
]
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True)
# Train the Model
epochs = 10
learning_rate = 0.01
train_chinnu_ai(model, quantum_trainer, target_state, dataloader, epochs, learning_rate)
```
### Live Chat with Chinnu AI
Chinnu AI can engage in real-time conversations based on JSON input:
```python
from ChinnuAi import chat_with_chinnu_ai
import json
# Example JSON input
json_input = '{"input": [1, 0, 0, 0], "responses": ["Hello!", "How can I assist?", "Here is your data.", "Goodbye!"]}'
response = chat_with_chinnu_ai(model, quantum_model, json_input)
print("Chinnu AI response:", response)
```
### Quantum State Manipulation
Chinnu AI allows you to directly manipulate quantum states for advanced computations:
```python
from ChinnuAi import QuantumState
# Initialize a Quantum State
qs = QuantumState([1, 0, 0, 0])
# Apply a Quantum Gate
qs.apply_gate(QuantumGates.H)
print("State after Hadamard Gate:", qs)
# Measure the State
measurement = qs.measure()
print("Measurement Outcome:", measurement)
```
### Example JSON Dataset
A sample JSON dataset for batch testing:
```json
{
"data": [
{
"input": [1, 0, 0, 0],
"responses": ["Welcome to Chinnu AI!", "How can I assist you today?", "Here is your data.", "Goodbye!"]
},
{
"input": [0, 1, 0, 0],
"responses": ["Hello again!", "Need assistance?", "Fetching details.", "See you soon!"]
}
]
}
```
### Batch Testing with JSON
```python
import json
from ChinnuAi import chat_with_chinnu_ai
# Load the JSON file
with open('test_data.json', 'r') as file:
test_data = json.load(file)
# Process each entry
for entry in test_data['data']:
json_input = json.dumps(entry)
response = chat_with_chinnu_ai(model, quantum_model, json_input)
print("Input:", entry["input"], "Response:", response)
```
## License
This project is licensed under the MIT License. See the LICENSE file for details.
## Contributing
Contributions are welcome! Open an issue or submit a pull request to improve Chinnu AI.
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"description": "# Chinnu AI with Quantum Integration \n# Gift for my best friend \n\n\nChinnu AI is a quantum-inspired chatbot framework that merges traditional deep learning methods with quantum computing concepts to deliver advanced conversational experiences.\n\n## Features\n\n- **Quantum State Representation**: Utilize `QuantumState` for quantum-inspired computations.\n- **Training Framework**: Train quantum-enhanced neural networks using `QuantumTrainer`.\n- **Dynamic Neural Network**: Leverage PyTorch-based deep learning for conversational AI.\n- **JSON-based Conversations**: Enable flexible interaction using structured JSON inputs.\n- **Modular Design**: Easily extend or integrate into existing systems.\n\n## Installation\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/yourusername/chinnu-ai.git\n cd chinnu-ai\n ```\n\n2. Install dependencies:\n ```bash\n pip install -r requirements.txt\n ```\n\n3. Install the package:\n ```bash\n pip install .\n ```\n\n## Usage\n\n### Training Chinnu AI\n\nHere is an example to train Chinnu AI with quantum integration:\n\n```python\nimport torch\nfrom ChinnuAi import QuantumState, QuantumTrainer, initialize_chinnu_ai, train_chinnu_ai\n\n# Initialize Quantum State and Trainer\ninitial_state = [1, 0, 0, 0]\ntarget_state = [0.5, 0.5, 0.5, 0.5]\nquantum_model = QuantumState(initial_state)\nquantum_trainer = QuantumTrainer(quantum_model, training_data=None)\n\n# Initialize Neural Network\ninput_size = 4\nhidden_size = 8\noutput_size = 4\nmodel = initialize_chinnu_ai(input_size, hidden_size, output_size)\n\n# Example Dataset\ndataset = [\n (torch.tensor([1, 0, 0, 0]), torch.tensor([0.5, 0.5, 0.5, 0.5])),\n (torch.tensor([0, 1, 0, 0]), torch.tensor([0.5, 0.5, 0.5, 0.5])),\n]\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True)\n\n# Train the Model\nepochs = 10\nlearning_rate = 0.01\ntrain_chinnu_ai(model, quantum_trainer, target_state, dataloader, epochs, learning_rate)\n```\n\n### Live Chat with Chinnu AI\n\nChinnu AI can engage in real-time conversations based on JSON input:\n\n```python\nfrom ChinnuAi import chat_with_chinnu_ai\nimport json\n\n# Example JSON input\njson_input = '{\"input\": [1, 0, 0, 0], \"responses\": [\"Hello!\", \"How can I assist?\", \"Here is your data.\", \"Goodbye!\"]}'\nresponse = chat_with_chinnu_ai(model, quantum_model, json_input)\nprint(\"Chinnu AI response:\", response)\n```\n\n### Quantum State Manipulation\n\nChinnu AI allows you to directly manipulate quantum states for advanced computations:\n\n```python\nfrom ChinnuAi import QuantumState\n\n# Initialize a Quantum State\nqs = QuantumState([1, 0, 0, 0])\n\n# Apply a Quantum Gate\nqs.apply_gate(QuantumGates.H)\nprint(\"State after Hadamard Gate:\", qs)\n\n# Measure the State\nmeasurement = qs.measure()\nprint(\"Measurement Outcome:\", measurement)\n```\n\n### Example JSON Dataset\n\nA sample JSON dataset for batch testing:\n\n```json\n{\n \"data\": [\n {\n \"input\": [1, 0, 0, 0],\n \"responses\": [\"Welcome to Chinnu AI!\", \"How can I assist you today?\", \"Here is your data.\", \"Goodbye!\"]\n },\n {\n \"input\": [0, 1, 0, 0],\n \"responses\": [\"Hello again!\", \"Need assistance?\", \"Fetching details.\", \"See you soon!\"]\n }\n ]\n}\n```\n\n### Batch Testing with JSON\n\n```python\nimport json\nfrom ChinnuAi import chat_with_chinnu_ai\n\n# Load the JSON file\nwith open('test_data.json', 'r') as file:\n test_data = json.load(file)\n\n# Process each entry\nfor entry in test_data['data']:\n json_input = json.dumps(entry)\n response = chat_with_chinnu_ai(model, quantum_model, json_input)\n print(\"Input:\", entry[\"input\"], \"Response:\", response)\n```\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n## Contributing\n\nContributions are welcome! Open an issue or submit a pull request to improve Chinnu AI.\n\n",
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