Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import MinMaxScaler
+import joblib
+
+# Load the pre-trained model and scaler
+model = joblib.load("water_quality_model.pkl")  # Ensure the model file is in the same directory
+scaler = joblib.load("scaler.pkl")
+
+# Define water quality categories
+def classify_quality(score):
+    if score >= 0.8:
+        return "Excellent"
+    elif score >= 0.6:
+        return "Good"
+    elif score >= 0.4:
+        return "Fair"
+    else:
+        return "Poor"
+
+# Define the prediction function
+def predict_water_quality(pH, turbidity, dissolved_oxygen, nitrate_levels):
+    # Prepare the input data
+    data = np.array([[pH, turbidity, dissolved_oxygen, nitrate_levels]])
+    scaled_data = scaler.transform(data)
+    
+    # Predict the quality score
+    score = model.predict(scaled_data)[0]
+    quality = classify_quality(score)
+
+    # Generate insights (graphical output)
+    plt.figure(figsize=(6, 4))
+    categories = ["Excellent", "Good", "Fair", "Poor"]
+    scores = [0.8, 0.6, 0.4, 0.2]
+    colors = ["green", "blue", "orange", "red"]
+
+    plt.bar(categories, scores, color=colors, alpha=0.7)
+    plt.axhline(y=score, color="black", linestyle="--", label=f"Predicted Score: {score:.2f} ({quality})")
+    plt.legend()
+    plt.title("Water Quality Prediction")
+    plt.xlabel("Categories")
+    plt.ylabel("Score")
+    plt.tight_layout()
+
+    # Save the graph to a file
+    graph_path = "water_quality_graph.png"
+    plt.savefig(graph_path)
+    plt.close()
+
+    return quality, graph_path
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict_water_quality,
+    inputs=[
+        gr.inputs.Number(label="pH"),
+        gr.inputs.Number(label="Turbidity (NTU)"),
+        gr.inputs.Number(label="Dissolved Oxygen (mg/L)"),
+        gr.inputs.Number(label="Nitrate Levels (mg/L)"),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Water Quality"),
+        gr.outputs.Image(label="Water Quality Graph")
+    ],
+    title="Water Quality Prediction",
+    description="Enter water parameters to predict the quality of water. Results include the classification and a visual representation of quality."
+)
+
+# Launch the application
+if __name__ == "__main__":
+    interface.launch()