Create app.py

app.py

@@ -0,0 +1,63 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+
+data = pd.read_csv("Placement_Data_Full_Class.csv")
+
+X = data.drop(["sl_no","status","salary"],axis=1)
+y = data["status"]
+
+X = pd.get_dummies(X,drop_first=True)
+y = pd.get_dummies(y,drop_first=True)
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+X = scaler.fit_transform(X)
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
+
+from sklearn.linear_model import LogisticRegression
+model = LogisticRegression()
+
+model.fit(X_train,y_train)
+
+def prediction(name,gender,ssc_p,ssc_b,hsc_p,hsc_b,hsc_s,degree_p,degree_t,workex,etest_p,specialisation,mba_p):
+    df = pd.DataFrame({"gender":gender,
+                       "ssc_p":float(ssc_p),"ssc_b":ssc_b,"hsc_p":int(hsc_p),"hsc_b":hsc_b,"hsc_s":hsc_s,
+                       "degree_p":float(degree_p),
+                       "degree_t":degree_t,"workex":workex,"etest_p":float(etest_p),"specialisation":specialisation,
+                       "mba_p":float(mba_p)
+                       },index=[0])
+    data = pd.read_csv("Placement_Data_Full_Class.csv")
+    data = data.drop(["sl_no","status","salary"],axis=1)
+    data = data.append(df,ignore_index = True)
+    data = pd.get_dummies(data,drop_first = True)
+    data = scaler.fit_transform(data)
+    var = model.predict(data[[-1]])
+    if var == 1:
+        return "Congratulations! "+name+", you have a high chance of getting placed."
+    else:
+        return "Sorry! "+name+", better luck next time."
+ 
+import gradio as gr
+       
+interface = gr.Interface(prediction,inputs=[
+    gr.Textbox(lines=2, placeholder="Enter your Name Here...", show_label = False),
+    gr.Dropdown(choices=["M","F"],value = "M",label = "Select your Gender"),
+    gr.Textbox(lines=2, placeholder="Enter your SSC Percentage Here...", show_label = False),
+    gr.Dropdown(choices=["Central","Others"],value = "Central",label = "Select your SSC Board"),
+    gr.Textbox(lines=2, placeholder="Enter your HSC Percentage Here...",show_label  = False),
+    gr.Dropdown(choices=["Central","Others"],value = "Others",label = "Select your HSC Board"),
+    gr.Dropdown(choices=["Commerce","Science","Arts"],value = "Commerce",label = "Select your HSC Stream"),
+    gr.Textbox(lines=2, placeholder="Enter your Degree Percentage Here...",show_label = False),
+    gr.Dropdown(choices=["Comm&Mgmt","Sci&Tech","Others"],value = "Comm&Mgmt",label = "Select your Degree Domain"),
+    gr.Dropdown(choices=["No","Yes"],value = "No",label = "Select Whether you have prior Work Experience"),
+    gr.Textbox(lines=2, placeholder="Enter your E Test Percentage Here...",show_label = False),
+    gr.Dropdown(choices=["Mkt&Fin","Mkt&HR"],value = "Mkt&Fin",label = "Select your Specialisation"),
+    gr.Textbox(lines=2, placeholder="Enter your MBA Percentage Here...",show_label = False)
+],outputs = gr.Label(value = "Prediction"),description = "Predicting Placement Chances")
+
+interface.launch()