Machine-learning/README.md

### **FastAPI AI**

This FastAPI app loads a GPT-2 model, tokenizes input text, classifies it, and returns whether the text is AI-generated or human-written.

### **install Dependencies**

```bash
pip install -r requirements.txt

```

This command installs all the dependencies listed in the `requirements.txt` file. It ensures that your environment has the required packages to run the project smoothly.

**NOTE: IF YOU HAVE DONE ANY CHANGES DON'NT FORGOT TO PUT IT IN THE REQUIREMENTS.TXT USING `bash pip freeze > requirements.txt `**

---

### **Functions**

1. **`load_model()`**  
   Loads the GPT-2 model and tokenizer from specified paths.

2. **`lifespan()`**  
   Manages the app's lifecycle: loads the model at startup and handles cleanup on shutdown.

3. **`classify_text_sync()`**  
   Synchronously tokenizes input text and classifies it using the GPT-2 model. Returns the classification and perplexity.

4. **`classify_text()`**  
   Asynchronously executes `classify_text_sync()` in a thread pool to ensure non-blocking processing.

5. **`analyze_text()`**  
   **POST** endpoint: accepts text input, classifies it using `classify_text()`, and returns the result with perplexity.

6. **`health_check()`**  
   **GET** endpoint: simple health check to confirm the API is running.

---

### **Code Overview**

```python
executor = ThreadPoolExecutor(max_workers=2)
```

- **`ThreadPoolExecutor(max_workers=2)`** limits the number of concurrent threads (tasks) per worker process to 2 for text classification. This helps control resource usage and prevent overloading the server.

---

### **Running and Load Balancing:**

To run the app in production with load balancing:

```bash
uvicorn app:app --host 0.0.0.0 --port 8000 --workers 4
```

This command launches the FastAPI app with **4 worker processes**, allowing it to handle multiple requests concurrently.

### **Concurrency Explained:**

1. **`ThreadPoolExecutor(max_workers=20)`**

   - Controls the **number of threads** within a **single worker** process.
   - Allows up to 20 tasks (text classification requests) to be handled simultaneously per worker, improving responsiveness for I/O-bound tasks.

2. **`--workers 4` in Uvicorn**
   - Spawns **4 independent worker processes** to handle incoming HTTP requests.
   - Each worker can independently handle multiple tasks, increasing the app's ability to process concurrent requests in parallel.

### **How They Relate:**

- **Uvicorn’s `--workers`** defines how many worker processes the server will run.
- **`ThreadPoolExecutor`** limits how many tasks (threads) each worker can process concurrently.

For example, with **4 workers** and **20 threads per worker**, the server can handle **80 tasks concurrently**. This provides scalable and efficient processing, balancing the load across multiple workers and threads.

### **Endpoints**

#### 1. **`/analyze`**

- **Method:** `POST`
- **Description:** Classifies whether the text is AI-generated or human-written.
- **Request:**
  ```json
  { "text": "sample text" }
  ```
- **Response:**
  ```json
  { "result": "AI-generated", "perplexity": 55.67 }
  ```

#### 2. **`/health`**

- **Method:** `GET`
- **Description:** Returns the status of the API.
- **Response:**
  ```json
  { "status": "ok" }
  ```

---

### **Running the API**

Start the server with:

```bash
uvicorn app:app --host 0.0.0.0 --port 8000 --workers 4
```

---

### **🧪 Testing the API**

You can test the FastAPI endpoint using `curl` like this:

```bash
curl -X POST http://127.0.0.1:8000/analyze \
  -H "Authorization: Bearer HelloThere" \
  -H "Content-Type: application/json" \
  -d '{"text": "This is a sample sentence for analysis."}'
```

- The `-H "Authorization: Bearer HelloThere"` part is used to simulate the **handshake**.
- FastAPI checks this token against the one loaded from the `.env` file.
- If the token matches, the request is accepted and processed.
- Otherwise, it responds with a `403 Unauthorized` error.

---

### **API Documentation**

- **Swagger UI:** `http://127.0.0.1:8000/docs` -> `/docs`
- **ReDoc:** `http://127.0.0.1:8000/redoc` -> `/redoc`

### **🔐 Handshake Mechanism**

In this part, we're implementing a simple handshake to verify that the request is coming from a trusted source (e.g., our NestJS server). Here's how it works:

- We load a secret token from the `.env` file.
- When a request is made to the FastAPI server, we extract the `Authorization` header and compare it with our expected secret token.
- If the token does **not** match, we immediately return a **403 Forbidden** response with the message `"Unauthorized"`.
- If the token **does** match, we allow the request to proceed to the next step.

The verification function looks like this:

```python
def verify_token(auth: str):
    if auth != f"Bearer {EXPECTED_TOKEN}":
        raise HTTPException(status_code=403, detail="Unauthorized")
```

This provides a basic but effective layer of security to prevent unauthorized access to the API.

### **Implement it with NEST.js**

NOTE: Make an micro service in NEST.JS and implement it there and call it from app.controller.ts

in fastapi.service.ts file what we have done is

### Project Structure

```files
nestjs-fastapi-bridge/
├── src/
│   ├── app.controller.ts
│   ├── app.module.ts
│   └── fastapi.service.ts
├── .env

```

---

### Step-by-Step Setup

#### 1. `.env`

Create a `.env` file at the root with the following:

```environment
  FASTAPI_BASE_URL=http://localhost:8000
  SECRET_TOKEN="HelloThere"
```

#### 2. `fastapi.service.ts`

```javascript
  // src/fastapi.service.ts
  import { Injectable } from "@nestjs/common";
  import { HttpService } from "@nestjs/axios";
  import { ConfigService } from "@nestjs/config";
  import { firstValueFrom } from "rxjs";

  @Injectable()
  export class FastAPIService {
    constructor(
      private http: HttpService,
      private config: ConfigService,
    ) {}

    async analyzeText(text: string) {
      const url = `${this.config.get("FASTAPI_BASE_URL")}/analyze`;
      const token = this.config.get("SECRET_TOKEN");

      const response = await firstValueFrom(
        this.http.post(
          url,
          { text },
          {
            headers: {
              Authorization: `Bearer ${token}`,
            },
          },
        ),
      );

      return response.data;
    }
  }
```

#### 3. `app.module.ts`

```javascript
// src/app.module.ts
import { Module } from "@nestjs/common";
import { ConfigModule } from "@nestjs/config";
import { HttpModule } from "@nestjs/axios";
import { AppController } from "./app.controller";
import { FastAPIService } from "./fastapi.service";

@Module({
  imports: [ConfigModule.forRoot(), HttpModule],
  controllers: [AppController],
  providers: [FastAPIService],
})
export class AppModule {}
```

---

#### 4. `app.controller.ts`

```javascript
  // src/app.controller.ts
  import { Body, Controller, Post, Get, Query } from '@nestjs/common';
  import { FastAPIService } from './fastapi.service';

  @Controller()
  export class AppController {
    constructor(private readonly fastapiService: FastAPIService) {}

    @Post('analyze-text')
    async callFastAPI(@Body('text') text: string) {
      return this.fastapiService.analyzeText(text);
    }

    @Get()
    getHello(): string {
      return 'NestJS is connected to FastAPI ';
    }
  }
```

### 🚀 How to Run

Run the server of flask and nest.js:

- for nest.js
  ```bash
  npm run start
  ```
- for Fastapi

  ```bash
  uvicorn app:app --reload
  ```

Make sure your FastAPI service is running at `http://localhost:8000`.

### Test with CURL

```bash
curl -X POST http://localhost:3000/analyze-text \
  -H 'Content-Type: application/json' \
  -d '{"text": "This is a test input"}'
```