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Random-Forest-Regressor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fcf9fc0ead5c22e855b1bbd75856946d09fe8c1396116e803753b7246e2b25f0
+size 16681902

app.py

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+import numpy as np
+import pandas as pd
+import pickle
+import gradio as gr
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import GridSearchCV
+from sklearn.ensemble import RandomForestRegressor
+
+# Load the saved full pipeline from the file
+model_file = '/media/penscola/Penscola@Tech/Projects/Gold-Prediction/model/Random-Forest-Regressor.pkl'
+
+with open(model_file, 'rb') as f_in:
+    scaler, model = pickle.load(f_in)
+
+# Define the predict function
+def predict(SPX, USO, SLV, EUR_USD):
+    # Create a DataFrame from the input data
+    input_data = pd.DataFrame({
+        'SPX': [SPX] if SPX is not None else [0],  # Replace None with default value
+        'USO': [USO] if USO is not None else [0],  # Replace None with default value
+        'SLV': [SLV] if SLV is not None else [0],  # Replace None with default value
+        'EUR_USD': [EUR_USD] if EUR_USD is not None else [0],  # Replace None with default value
+    })
+
+
+        # Make predictions using the loaded logistic regression model
+        #predict probabilities
+    predictions = model.predict(input_data)
+    #take the index of the maximum probability
+
+
+    #return predictions[0]
+    return(f'[Info] Predicted probabilities{predictions}')
+    
+# Setting Gradio App Interface
+with gr.Blocks(css=".gradio-container {background-color:grey }",theme=gr.themes.Base(primary_hue='blue'),title='Uriel') as demo:
+    gr.Markdown("# Gold Price prediction #\n*This App allows the user to predict the price of Gold.*")
+    
+    # Receiving ALL Input Data here
+    gr.Markdown("**Demographic Data**")
+    with gr.Row():
+        gender = gr.Number(label="Standard & Poor's Index")
+        SeniorCitizen = gr.Number(label="United State Oil Fund")
+        Partner = gr.Number(label="Silver Price")
+        Dependents = gr.Number(label="EURO_Dollar Exchange")
+
+
+    # Output Prediction
+    output = gr.Text(label="Outcome")
+    submit_button = gr.Button("Predict")
+    
+    submit_button.click(fn= predict,
+                        outputs= output,
+                        inputs=[gender, SeniorCitizen, Partner, Dependents],
+    
+    ),
+    
+    # Add the reset and flag buttons
+    def clear():
+        output.value = ""
+        return 'Predicted values have been reset'
+         
+    clear_btn = gr.Button("Reset", variant="primary")
+    clear_btn.click(fn=clear, inputs=None, outputs=output)
+        
+ 
+demo.launch(inbrowser = True)

requirements.txt

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+sklearn==0.0.post9
+pandas==2.1.1
+numpy==1.25.2
+seaborn==0.12.2
+matplotlib==3.8.0