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app.py

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+#gradio app 
+
+import gradio as gr
+import pandas as pd
+import pickle
+import numpy as np
+
+# 1. Load the Model
+with open("student_rf_pipeline.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# 2. The Logic Function
+def predict_gpa(gender, age, address, famsize, 
+                Pstatus, M_Edu, F_Edu, M_Job, F_Job, 
+                relationship, smoker, tuition_fee, time_friends,
+                  ssc_result):
+    
+    # Pack inputs into a DataFrame
+    # The column names must match your CSV file exactly
+    input_df = pd.DataFrame([[
+        gender, age, address, famsize, Pstatus, 
+        M_Edu, F_Edu, M_Job, F_Job, relationship, 
+        smoker, tuition_fee, time_friends, ssc_result
+
+    ]],
+      columns=[
+        'gender', 'age', 'address', 'famsize', 'Pstatus', 'M_Edu', 'F_Edu', 'M_Job', 'F_Job', 'relationship', 'smoker', 'tuition_fee', 'time_friends', 'ssc_result'
+    ])
+    
+    # Predict
+    prediction = model.predict(input_df)[0]
+    
+    # Return formatted result (Clipped 0-5)
+    return f"Predicted HSC Result: {np.clip(prediction, 0, 5):.2f}"
+
+# 3. The App Interface
+# Defining inputs in a list to keep it clean
+inputs = [
+    gr.Radio(["M", "F"], label="Gender"),
+    gr.Number(label="Age", value=18),
+    gr.Radio(["Urban", "Rural"], label="Address"),
+    gr.Radio(["GT3", "LE3"], label="Family Size"),
+    gr.Radio(["Together", "Apart"], label="Parent Status"),
+    gr.Slider(0, 4, step=1, label="Mother's Edu"),
+    gr.Slider(0, 4, step=1, label="Father's Edu"),
+    gr.Dropdown(["At_home", "Health", "Other", "Services", "Teacher"], label="Mother's Job"),
+    gr.Dropdown(["Teacher", "Other", "Services", "Health", "Business", "Farmer"], label="Father's Job"),
+    gr.Radio(["Yes", "No"], label="Relationship"),
+    gr.Radio(["Yes", "No"], label="Smoker"),
+    gr.Number(label="Tuition Fee"),
+    gr.Slider(1, 5, step=1, label="Time with Friends"),
+    gr.Number(label="SSC Result (GPA)")
+]
+
+app = gr.Interface(
+    fn=predict_gpa,
+      inputs=inputs,
+        outputs="text", 
+        title="HSC Predictor")
+
+app.launch(share=True)

requirements.txt

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+aiofiles==24.1.0
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.12.1
+audioop-lts==0.2.2
+brotli==1.2.0
+certifi==2026.1.4
+click==8.3.1
+colorama==0.4.6
+fastapi==0.128.0
+ffmpy==1.0.0
+filelock==3.20.3
+fsspec==2026.1.0
+gradio==6.5.1
+gradio_client==2.0.3
+groovy==0.1.2
+h11==0.16.0
+hf-xet==1.2.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface_hub==1.3.5
+idna==3.11
+Jinja2==3.1.6
+joblib==1.5.3
+markdown-it-py==4.0.0
+MarkupSafe==3.0.3
+mdurl==0.1.2
+numpy==2.4.1
+orjson==3.11.6
+packaging==26.0
+pandas==3.0.0
+pillow==12.1.0
+pydantic==2.12.5
+pydantic_core==2.41.5
+pydub==0.25.1
+Pygments==2.19.2
+python-dateutil==2.9.0.post0
+python-multipart==0.0.22
+pytz==2025.2
+PyYAML==6.0.3
+rich==14.3.1
+safehttpx==0.1.7
+scikit-learn==1.8.0
+scipy==1.17.0
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+starlette==0.50.0
+threadpoolctl==3.6.0
+tomlkit==0.13.3
+tqdm==4.67.2
+typer==0.21.1
+typer-slim==0.21.1
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.3
+uvicorn==0.40.0

student_rf_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f37abb886223ab7f401134544852875c27636f2d26cfc3122ed77437cfa4963
+size 14731925