Oky the project is learn how to develop a classification model , dump it using joblib 
Load it and create an efficient endpoint using fast api 


Oky 


We start with the most simples model of classification using this data set 


About the Dataset
Context
Based on Fisher's linear discriminant model, this Iris dataset serves as a typical test case for various statistical classification techniques in machine learning, such as support vector machines.
Content
The Iris flower dataset, also known as Fisher's Iris dataset, is a multivariate dataset introduced by British statistician and biologist Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems." It exemplifies linear discriminant analysis. Sometimes called Anderson's Iris dataset, it was Edgar Anderson who collected the data to quantify the morphological variation of Iris flowers across three related species. Two of these species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus."
The dataset comprises 50 samples from each of the three Iris species: Iris setosa, Iris virginica, and Iris versicolor. Four features were measured for each sample: the length and width of both the sepals and petals, all in centimeters. Using these four features, Fisher developed a linear discriminant model to differentiate the species.
Acknowledgements
Dataset description adapted from Wikipedia. Special thanks to Dr. <PRESIDIO_ANONYMIZED_PERSON> for his clear and insightful explanations of related examples.




About Dataset
It includes three iris species with 50 samples each as well as some properties of each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
FIle name: iris.csv
About this file
add_commentAdd Suggestion
Attribute Information:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
-- Iris Setosa
-- Iris Versicolour
-- Iris Virginica
grid_3x3sepal_lengthsort
sepal length in cm
grid_3x3sepal_widthsort
sepal width in cm
grid_3x3petal_lengthsort
petal length in cm
grid_3x3petal_widthsort
petal width in cm
text_formatspeciessort
class


Q- now create a discussion between two experts about the dataset description oky 
About Dataset
The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
Number of Instances: 150
Number of Features: 5
   * Sepal Length (in cm, numeric)
   * Sepal Width (in cm, numeric)
   * Petal Length (in cm, numeric)
   * Petal Width (in cm, numeric)
   * Species (categorical: Setosa, Versicolor, Virginica)
Target Variable:
   * Species (3 classes: Setosa, Versicolor, Virginica)
Feature Types:
   * 4 Continuous Numeric Features
   * 1 Categorical Feature (Target)
Missing Values: None
Class Distribution:
   * Setosa: 50 instances
   * Versicolor: 50 instances
   * Virginica: 50 instances
Context: This dataset is widely used for classification tasks and exploratory data analysis (EDA) in machine learning, especially in demonstrating simple models like k-nearest neighbors (KNN), decision trees, and linear discriminants.
Primary Use Cases:
   * Multiclass classification
   * Pattern recognition
   * Statistical analysis for educational purposes
License: Public domain


Q- first step explore the data oky ? 

Like this 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns




df = sns.load_dataset("iris")
df.head()




sepal_length
sepal_width
petal_length
petal_width
species
0
5.1
3.5
1.4
0.2
setosa
1
4.9
3.0
1.4
0.2
setosa
2
4.7
3.2
1.3
0.2
setosa
3
4.6
3.1
1.5
0.2
setosa
4
5.0
3.6
1.4
0.2
setosa



df.tail()


sepal_length
sepal_width
petal_length
petal_width
species


145
6.7
3.0
5.2
2.3
virginica
146
6.3
2.5
5.0
1.9
virginica
147
6.5
3.0
5.2
2.0
virginica
148
6.2
3.4
5.4
2.3
virginica
149
5.9
3.0
5.1
1.8
virginica



Q- next we move the data exmplotry analysis oky 

Like this 

df.shape
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   sepal_length  150 non-null    float64
 1   sepal_width   150 non-null    float64
 2   petal_length  150 non-null    float64
 3   petal_width   150 non-null    float64
 4   species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB
df.describe()

sepal_length
sepal_width
petal_length
petal_width


count
150.000000
150.000000
150.000000
150.000000
mean
5.843333
3.057333
3.758000
1.199333
std
0.828066
0.435866
1.765298
0.762238
min
4.300000
2.000000
1.000000
0.100000
25%
5.100000
2.800000
1.600000
0.300000
50%
5.800000
3.000000
4.350000
1.300000
75%
6.400000
3.300000
5.100000
1.800000



We have done the full eda analysis and come to these conclusion oky 
nsights from EDA on the Iris Dataset¶
Petal length and petal width are strong indicators for distinguishing species.
Setosa is easily separable with smaller petal measurements.
Versicolor and Virginica overlap but can be distinguished by petal measurements.
Sepal length and sepal width show overlap and are less effective at distinguishing species.
Petal length and width have non-overlapping regions for the species.
Sepal width has a wide spread, making it less useful in classification.
Boxplots show Setosa has a tighter distribution for sepal and petal length, while Versicolor and Virginica show variability.
Petal length and width are highly positively correlated.
Sepal width negatively correlates with petal measurements, limiting its usefulness.
The dataset is balanced across the three species, with no class imbalance.
Petal length and petal width show clear clusters for each species, making them useful for classification.
Setosa has the smallest sepal length, Virginica the largest, with Versicolor and Virginica showing overlap.
Setosa often has a higher sepal width, with significant overlap between Versicolor and Virginica.
Sepal width and length show little correlation and overlap between species.
Petal length and width show clear species separation, while sepal width has variability.
Outliers are visible, especially in Virginica's sepal width, needing attention in preprocessing.
Andrews curves show Setosa as distinct, while Versicolor and Virginica overlap.
Categorical plots confirm Setosa's clear separation by petal length and width.
Violin plots show tightly grouped features for Setosa, while Versicolor and Virginica have wider distributions.
Petal measurements are key for classification, while sepal features show overlap between species.
Outliers, particularly in Virginica's sepal width, may impact model training.
Setosa is distinct, while Versicolor and Virginica show some overlap, especially in sepal features.


Q-
Next i create  a python file called logistic_regression.py like this 
“import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression


df = sns.load_dataset("iris")




scaler = StandardScaler()


X_scaled = scaler.fit_transform(df.drop('species',axis=1))
# Update the dataframe
df_scaled = pd.DataFrame(X_scaled, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
# add the species column back to the scaled dataframe
df_scaled['species'] = df['species']




"""
Data Spliting


"""


X=df_scaled.drop("species",axis=1)
y=df_scaled["species"]


# train test spit the data
X_train ,X_test , y_train , y_test = train_test_split(X,y,test_size=0.2,random_state=42)


"""
implement the first model
- a Logistic Regression model
"""


log_req = LogisticRegression()
log_req.fit(X_train,y_train)


log_req.score(X_test,y_test)
‘ 
Q- first did the logic of the model look oky or did i need to add something before joblib it ? 

**Review and Recommendations for Your Logistic Regression Model on the Iris Dataset**

Your approach to building the logistic regression model for the Iris dataset is well-structured and follows standard machine learning practices. However, here are a few enhancements and best practices to consider before saving your model with `joblib`. This detailed review provides some key recommendations:

### 1. Model Evaluation Enhancements
Using `model.score` to check the accuracy on the test set is a good start, but additional metrics can provide a more comprehensive understanding of performance, especially for multiclass classification.

**Recommendations:**
- **Confusion Matrix:** Visualizes the model's performance in terms of correctly and incorrectly classified instances.
- **Classification Report:** Gives precision, recall, and F1-score for each class.
- **Cross-Validation:** Provides a more robust estimate of the model's performance.

**Example:**

```python
from sklearn.metrics import classification_report, confusion_matrix

# Predictions
y_pred = log_reg.predict(X_test)

# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:\n", conf_matrix)

# Classification Report
class_report = classification_report(y_test, y_pred)
print("Classification Report:\n", class_report)
```

### 2. Incorporate a Pipeline
Integrating preprocessing steps with the model into a single pipeline ensures that the same transformations are applied during both training and inference, minimizing the risk of data leakage.

**Example:**

```python
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# Create a pipeline with scaling and logistic regression
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('logistic_regression', LogisticRegression())
])

# Train the pipeline
pipeline.fit(X_train, y_train)

# Evaluate the pipeline
accuracy = pipeline.score(X_test, y_test)
print(f"Model Accuracy: {accuracy}")

# Save the pipeline
import joblib
joblib.dump(pipeline, 'logistic_regression_model.joblib')
```

### 3. Save Additional Components
If not using a pipeline, ensure that you save not only the model but also preprocessing objects (e.g., scaler) to ensure consistency during inference.

**Example:**

```python
# Save the scaler and the model separately
joblib.dump(scaler, 'scaler.joblib')
joblib.dump(log_reg, 'logistic_regression_model.joblib')
```

### 4. Handling Categorical Variables
The Iris dataset's target variable is categorical, and logistic regression in scikit-learn handles it if encoded properly. For more complex categorical features in future projects, consider using encoding techniques.

### 5. Hyperparameter Tuning
To improve model performance, explore hyperparameter tuning using techniques like Grid Search or Random Search.

**Example:**

```python
from sklearn.model_selection import GridSearchCV

# Define parameter grid
param_grid = {
    'logistic_regression__C': [0.1, 1, 10, 100],
    'logistic_regression__solver': ['liblinear', 'lbfgs']
}

# Create GridSearchCV
grid = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')

# Fit GridSearchCV
grid.fit(X_train, y_train)

# Best parameters and score
print(f"Best Parameters: {grid.best_params_}")
print(f"Best Cross-Validation Score: {grid.best_score_}")

# Save the best model
joblib.dump(grid.best_estimator_, 'best_logistic_regression_model.joblib')
```

### 6. Final Code Integration
Here’s a complete version of your logistic regression model with the enhancements:

```python
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report, confusion_matrix
import joblib

# Load the dataset
df = sns.load_dataset("iris")

# Features and target
X = df.drop("species", axis=1)
y = df["species"]

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('logistic_regression', LogisticRegression(multi_class='multinomial', solver='lbfgs'))
])

# Train the model
pipeline.fit(X_train, y_train)

# Evaluate the model
accuracy = pipeline.score(X_test, y_test)
print(f"Model Accuracy: {accuracy}")

# Detailed evaluation
y_pred = pipeline.predict(X_test)
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

# Save the model
joblib.dump(pipeline, 'logistic_regression_model.joblib')
print("Model saved successfully!")
```

### 7. Preparing for Deployment with FastAPI
When deploying with FastAPI, using a pipeline simplifies the process since all preprocessing steps are encapsulated within it. This ensures that incoming data is processed consistently before making predictions.

**FastAPI Integration Example:**

```python
from fastapi import FastAPI
import joblib
from pydantic import BaseModel
import pandas as pd

# Load the saved pipeline
model = joblib.load('logistic_regression_model.joblib')

# Define the request schema
class IrisFeatures(BaseModel):
    sepal_length: float
    sepal_width: float
    petal_length: float
    petal_width: float

# Initialize FastAPI
app = FastAPI()

@app.post("/predict")
def predict_species(features: IrisFeatures):
    # Convert input data to DataFrame
    input_data = pd.DataFrame([features.dict().values()], columns=features.dict().keys())
    # Make prediction
    prediction = model.predict(input_data)[0]
    return {"predicted_species": prediction}
```

### Conclusion
Your current implementation is solid for a basic logistic regression model. By incorporating these enhancements, you can improve the robustness, maintainability, and deployment readiness of your model. Integrating a pipeline is especially beneficial as it streamlines both training and inference processes, making your workflow more efficient and less error-prone.

Feel free to reach out if you have any more questions or need further assistance!


Q- wa just refacror the code to become like this oky 

“import os
import pandas as pd
import seaborn as sns
import joblib
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report, confusion_matrix

def load_data():
    """Load and prepare the Iris dataset."""
    df = sns.load_dataset("iris")
    X = df.drop("species", axis=1)
    y = df["species"]
    return train_test_split(X, y, test_size=0.2, random_state=42)

def create_pipeline():
    """Create a pipeline with StandardScaler and LogisticRegression."""
    return Pipeline([
        ('scaler', StandardScaler()),
        ('logistic_regression', LogisticRegression(multi_class='multinomial', solver='lbfgs'))
    ])

def train_and_evaluate_model(pipeline, X_train, X_test, y_train, y_test):
    """Train the model and print evaluation metrics."""
    pipeline.fit(X_train, y_train)
    accuracy = pipeline.score(X_test, y_test)
    print(f"Model Accuracy: {accuracy:.4f}")
    
    y_pred = pipeline.predict(X_test)
    print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
    print("Classification Report:\n", classification_report(y_test, y_pred))

def save_model(pipeline, filename='logistic_regression_model.joblib'):
    """Save the trained model to a file."""
    os.makedirs('models', exist_ok=True)
    model_path = os.path.join('models', filename)
    joblib.dump(pipeline, model_path)
    print(f"Model saved successfully in {model_path}!")

def main():
    # Load and split the data
    X_train, X_test, y_train, y_test = load_data()
    
    # Create and train the model
    pipeline = create_pipeline()
    train_and_evaluate_model(pipeline, X_train, X_test, y_train, y_test)
    
    # Save the model
    save_model(pipeline)

if __name__ == "__main__":
    main()
“ 

Q- now it save it in this folder 
Models
→models\logistic_regression_model.joblib
Q- next we need to implement a hight quality fast api endpoint ? 

Did you think this a good first iteration ? 

from fastapi import FastAPI
import joblib
from pydantic import BaseModel
import pandas as pd


# Load the saved pipline
model = joblib.load('models/logistic_regression_model.joblib')


# Define the request schema
class IrisFeatures(BaseModel):
    sepal_length: float
    sepal_width: float
    petal_length: float
    petal_width: float


# Initialize the FastAPI app
app = FastAPI()


def predict_sepcies(features: IrisFeatures):


    input_data = pd.DataFrame([features.dict().values()], columns=features.dict().keys())
   
    prediction = model.predict(input_data)[0]


    return {"prediction": prediction}

Sure! Here's the formatted version of your text:

---

**Your journey to developing a classification model and deploying it with FastAPI is progressing well! Let's review your FastAPI endpoint and discuss ways to enhance its quality for production readiness.**

### Review of Your Current FastAPI Implementation

You've provided the following FastAPI code:

```python
from fastapi import FastAPI
import joblib
from pydantic import BaseModel
import pandas as pd

# Load the saved pipeline
model = joblib.load('models/logistic_regression_model.joblib')

# Define the request schema
class IrisFeatures(BaseModel):
    sepal_length: float
    sepal_width: float
    petal_length: float
    petal_width: float

# Initialize the FastAPI app
app = FastAPI()

@app.post("/predict")
def predict_species(features: IrisFeatures):
    input_data = pd.DataFrame([features.dict().values()], columns=features.dict().keys())
    prediction = model.predict(input_data)[0]
    return {"prediction": prediction}
```

### Positive Aspects:
- **Clear Structure:** The code is well-organized with clear sections for loading the model, defining the data schema, and setting up the API endpoint.
- **Use of Pydantic:** Leveraging Pydantic’s `BaseModel` ensures that input data is validated automatically.
- **Pipeline Utilization:** Loading the entire pipeline (StandardScaler + LogisticRegression) simplifies preprocessing during inference.

### Areas for Improvement:
1. **Error Handling:** The current implementation lacks error handling, which can lead to ungraceful failures if unexpected input is received.
2. **Type Annotations and Async Support:** While not mandatory, using type annotations and making the endpoint asynchronous (`async`) can enhance performance and clarity.
3. **Input Validation Enhancements:** Adding constraints (e.g., value ranges) ensures that inputs fall within expected parameters.
4. **Logging:** Implementing logging aids in monitoring and debugging.
5. **Environment Configuration:** Managing configurations (like model paths) through environment variables increases flexibility.
6. **Documentation and Testing:** Providing documentation and incorporating tests ensures reliability and ease of use.
7. **Security Considerations:** While not critical for a simple demo, considering security best practices is essential for production systems.

---

### Enhanced FastAPI Implementation

Below is an improved version of your FastAPI endpoint incorporating best practices:

```python
import os
import logging
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field, ValidationError
import joblib
import pandas as pd
from typing import Optional

# Configure Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load environment variables or set defaults
MODEL_PATH = os.getenv('MODEL_PATH', 'models/logistic_regression_model.joblib')

# Initialize FastAPI app
app = FastAPI(
    title="Iris Species Prediction API",
    version="1.0.0",
    description="An API to predict Iris species based on flower measurements."
)

# Load the saved pipeline
try:
    model = joblib.load(MODEL_PATH)
    logger.info(f"Model loaded successfully from {MODEL_PATH}")
except FileNotFoundError:
    logger.error(f"Model file not found at {MODEL_PATH}.")
    raise
except Exception as e:
    logger.error(f"Error loading the model: {e}")
    raise

# Define the request schema with validations
class IrisFeatures(BaseModel):
    sepal_length: float = Field(..., gt=0, description="Sepal length in centimeters")
    sepal_width: float = Field(..., gt=0, description="Sepal width in centimeters")
    petal_length: float = Field(..., gt=0, description="Petal length in centimeters")
    petal_width: float = Field(..., gt=0, description="Petal width in centimeters")

# Custom Exception Handler for Validation Errors
@app.exception_handler(ValidationError)
async def validation_exception_handler(request: Request, exc: ValidationError):
    logger.error(f"Validation error: {exc}")
    return JSONResponse(
        status_code=422,
        content={"detail": exc.errors(), "body": exc.body},
    )

# Root Endpoint
@app.get("/", summary="Root Endpoint", description="Welcome to the Iris Species Prediction API.")
def read_root():
    return {"message": "Welcome to the Iris Species Prediction API. Use /predict to get predictions."}

# Prediction Endpoint
@app.post("/predict", summary="Predict Iris Species", description="Predict the Iris species based on flower measurements.")
async def predict_species(features: IrisFeatures):
    try:
        # Convert input data to DataFrame
        input_data = pd.DataFrame([features.dict().values()], columns=features.dict().keys())
        logger.info(f"Received input data: {input_data.to_dict(orient='records')}")

        # Make prediction
        prediction = model.predict(input_data)[0]
        logger.info(f"Prediction: {prediction}")

        return {"prediction": prediction}
    except Exception as e:
        logger.error(f"Error during prediction: {e}")
        raise HTTPException(status_code=500, detail="Internal Server Error")

# Health Check Endpoint
@app.get("/health", summary="Health Check", description="Health check to verify if the API is running.")
def health_check():
    return {"status": "API is running"}

# Example Response Model (Optional)
class PredictionResponse(BaseModel):
    prediction: str

# Update predict_species to use Response Model
@app.post("/predict", response_model=PredictionResponse, summary="Predict Iris Species", description="Predict the Iris species based on flower measurements.")
async def predict_species(features: IrisFeatures):
    try:
        # Convert input data to DataFrame
        input_data = pd.DataFrame([features.dict().values()], columns=features.dict().keys())
        logger.info(f"Received input data: {input_data.to_dict(orient='records')}")

        # Make prediction
        prediction = model.predict(input_data)[0]
        logger.info(f"Prediction: {prediction}")

        return PredictionResponse(prediction=prediction)
    except Exception as e:
        logger.error(f"Error during prediction: {e}")
        raise HTTPException(status_code=500, detail="Internal Server Error")
```

---

### Enhancements Explained

- **Logging:** Configured logging to track the application's behavior, including model loading, incoming requests, predictions, and errors.
- **Environment Configuration:** Utilized environment variables for model path configuration, enhancing flexibility across different environments (development, testing, production).
- **Input Validation:** Added constraints to input fields (e.g., `gt=0` ensures that measurements are positive).
- **Exception Handling:** Added a custom exception handler for validation errors and wrapped the prediction logic in a `try-except` block for graceful error handling.
- **API Metadata:** Added title, version, and description to the FastAPI app and provided summaries for each endpoint to improve the autogenerated documentation.
- **Async Support:** Made the endpoint asynchronous to improve scalability, especially under high load.
- **Health Check Endpoint:** Added a `/health` endpoint to monitor the API’s status.
- **Response Models:** Standardized the API responses using `PredictionResponse` to ensure consistency and clarity.

---

### Testing Your API

- **Run the API:**

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

- **Access Documentation:** Open `http://127.0.0.1:8000/docs` for the Swagger UI.
- **Test the Prediction Endpoint:** 

  ```json
  {
    "sepal_length": 5.1,
    "sepal_width": 3.5,
    "petal_length": 1.4,
    "petal_width": 0.2
  }
  ```

  You should receive a prediction like:

  ```json
  {
    "prediction": "setosa"
  }
  ```

- **Test Error Handling:** Send invalid data (e.g., negative values) to ensure validation works properly.

---

### Next Steps for Deployment

- **Dockerization:** Containerize your FastAPI application using Docker.
- **CI/CD:** Set up pipelines to automate testing and deployment.
- **Scalability:** Deploy using platforms like Kubernetes or AWS ECS.
- **Security:** Implement authentication, ensure data is transmitted over HTTPS, and follow security best practices.
- **Documentation:** Maintain detailed documentation for both developers and end-users.

---

### Conclusion

Your FastAPI endpoint is a solid foundation. By incorporating these enhancements, you can elevate it to a production-ready state, ensuring reliability, scalability, and maintainability. Keep iterating based on feedback and testing, and you'll have a robust deployment ready for real-world applications!

Feel free to reach out for further assistance!
To test your FastAPI endpoints using **Insomnia**, follow these steps:

### 1. **Open Insomnia**

If you haven't already installed Insomnia, you can download it from [insomnia.rest](https://insomnia.rest/download).

### 2. **Create a New Request**

1. Open Insomnia and click on **New Request**.
2. Name the request something like `Predict Iris Species`.
3. Choose **POST** as the request method.

### 3. **Set the URL**

- Set the URL to `http://127.0.0.1:8000/predict`.

### 4. **Set the Body**

1. Click on the **Body** tab.
2. Choose **JSON** from the dropdown.
3. Add the following JSON to the body:

```json
{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2
}
```

### 5. **Send the Request**

Click the **Send** button.

### Expected Response:

You should get a response similar to:

```json
{
  "prediction": "setosa"
}
```

### 6. **Test Error Handling (Optional)**

To test how the API handles invalid input, follow the same steps but modify the JSON body with invalid data. For example:

```json
{
  "sepal_length": -1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2
}
```

After clicking **Send**, you should get a validation error response like:

```json
{
  "detail": [
    {
      "loc": ["body", "sepal_length"],
      "msg": "ensure this value is greater than 0",
      "type": "value_error.number.not_gt",
      "ctx": {"limit_value": 0}
    }
  ]
}
```

### 7. **Test the `/health` Endpoint**

1. Create a new request in Insomnia, name it something like `API Health Check`.
2. Set the method to **GET**.
3. Set the URL to `http://127.0.0.1:8000/health`.
4. Click **Send**.

You should receive the following response:

```json
{
  "status": "API is running"
}
```

By following these steps, you can easily test your FastAPI application in Insomnia.

Q- next step i want first to save my depencesi in the virtual env that i create a requirement.txt file 
Q- then my question how to push it in my repo like this 
echo "# Mlops-Fast-Api" >> README.md
git init
git add README.md
git commit -m "first commit"
git branch -M main
git remote add origin https://github.com/AmirAbda/Mlops-Fast-Api.git
git push -u origin main

Q- did i need to add my virtual env in .gitignore yes or non ? 

Q- give step by step tutor to answer my questions oky