Update app.py

app.py

@@ -0,0 +1,45 @@
+import pickle
+from sklearn.linear_model import LogisticRegression
+import gradio as gr
+import numpy as np
+
+with open('logreg_model.pkl', "rb") as file:
+    loaded_model = pickle.load(file)
+    
+def predict_admission(gre_score, toefl_score, university_rating, sop, lor, cgpa, research, threshold=0.5):
+    # Convert 'Yes'/'No' to 1/0 for the 'Research' field
+    research = 1 if research == "Yes" else 0
+
+    # Create an input array from the provided values
+    input_data = np.array([[1, gre_score, toefl_score, university_rating, sop, lor, cgpa, research]])  # Added a 1 for the intercept
+
+    # Make a prediction
+    prediction_probability = loaded_model.predict(input_data)[0]
+    prediction = 'Admit' if prediction_probability >= threshold else 'No Admit'
+
+    # Custom formatting for output
+    prediction_color = "green" if prediction == 'Admit' else "red"
+    result = f"<div style='font-size: 24px; color: {prediction_color}; font-weight: bold; font-family: Arial Black;'>Admission Prediction: {prediction}</div>"
+    result += f"<br>Probability: {prediction_probability:.2f}"
+    result += f"<br>Threshold Used: {threshold}"
+
+    return result
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_admission,
+    inputs=[
+        gr.Number(label="GRE Score"),  # Set maximum GRE score
+        gr.Number(label="TOEFL Score"),
+        gr.Slider(minimum=1, maximum=5, label="University Rating"),
+        gr.Slider(minimum=1, maximum=5, label="SOP"),
+        gr.Slider(minimum=1, maximum=5, label="LOR"),
+        gr.Number(label="CGPA"),
+        gr.Radio(choices=["Yes", "No"], label="Research", value="No"),
+        gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label="Threshold")
+    ],
+    outputs=gr.HTML(label="Prediction"),
+    allow_flagging="never"
+)
+
+iface.launch()