Update app.py

app.py

@@ -10,16 +10,10 @@ import requests
 # Read the data
 data = pd.read_csv("mldata.csv")
 
-# Function to load model based on selection
-def load_model(model_choice):
-    if model_choice == "Random Forest":
-        with open('rfweights.pkl', 'rb') as pickleFile:
-            return pickle.load(pickleFile)
-    elif model_choice == "Decision Tree":
-        with open('dtreeweights.pkl', 'rb') as pickleFile:
-            return pickle.load(pickleFile)
-    else:
-        raise ValueError("Invalid model selection")
+# Function to load model
+def load_model():
+    with open('rfweights.pkl', 'rb') as pickleFile:
+        return pickle.load(pickleFile)
 
 # Prepare categorical data 
 categorical_cols = data[[
@@ -216,14 +210,14 @@ def fetch_job_listings(job_title):
     ]
 
 # Prediction function (modified to return job suggestions)
-def rfprediction(model_choice, name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
+def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills,
                  self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability,
                  subject_interest, career_interest, company_intend, senior_elder_advise, book_interest, introvert_extro,
                  team_player, management_technical, smart_hardworker):
     
     try:
-        # Load the selected model
-        rfmodel = load_model(model_choice)
+        # Load the Random Forest model
+        rfmodel = load_model()
         
         # Create DataFrame 
         df = pd.DataFrame({
@@ -347,7 +341,6 @@ def create_output_component():
 demo = gr.Interface(
     fn=rfprediction, 
     inputs=[
-        gr.Dropdown(["Random Forest", "Decision Tree"], label="Select Machine Learning Model"),
         gr.Textbox(placeholder="What is your name?", label="Name"),
         gr.Slider(minimum=1, maximum=9, value=3, step=1, label="Are you a logical thinking person?", info="Scale: 1 - 9"),
         gr.Slider(minimum=0, maximum=6, value=0, step=1, label="Do you attend any Hackathons?", info="Scale: 0 - 6 | 0 - if not attended any"),