Update app.py

app.py

@@ -1,64 +1,70 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import joblib
+import numpy as np
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load the pre-trained model
+model = joblib.load("model_loan_predector.pkl")
 
+# Define prediction function
+def predict_loan(gender, married, education, self_employed, applicant_income, coapplicant_income,
+                 loan_amount, loan_term, credit_history, property_area):
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    # Encode inputs manually (simulate label encoding)
+    input_data = np.array([[
+        1 if gender == "Male" else 0,
+        1 if married == "Yes" else 0,
+        1 if education == "Graduate" else 0,
+        1 if self_employed == "Yes" else 0,
+        float(applicant_income),
+        float(coapplicant_income),
+        float(loan_amount),
+        float(loan_term),
+        int(credit_history),
+        {"Rural": 0, "Semiurban": 1, "Urban": 2}[property_area]
+    ]])
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Predict
+    prediction = model.predict(input_data)[0]
+    prob = model.predict_proba(input_data)[0][1]
 
-    messages.append({"role": "user", "content": message})
+    # Risk Level
+    if prob > 0.8:
+        risk = "Low Risk"
+    elif prob > 0.5:
+        risk = "Medium Risk"
+    else:
+        risk = "High Risk"
 
-    response = ""
+    result = f"✅ Approved" if prediction == 1 else "❌ Not Approved"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    return {
+        "Loan Status": result,
+        "Approval Probability": f"{prob:.2%}",
+        "Risk Category": risk
+    }
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_loan,
+    inputs=[
+        gr.Radio(["Male", "Female"], label="Gender"),
+        gr.Radio(["Yes", "No"], label="Married"),
+        gr.Radio(["Graduate", "Not Graduate"], label="Education"),
+        gr.Radio(["Yes", "No"], label="Self Employed"),
+        gr.Number(label="Applicant Income"),
+        gr.Number(label="Coapplicant Income"),
+        gr.Number(label="Loan Amount (in ₹1000s)"),
+        gr.Number(label="Loan Term (in Days)"),
+        gr.Radio(["1", "0"], label="Credit History (1 = Good, 0 = Bad)"),
+        gr.Radio(["Rural", "Semiurban", "Urban"], label="Property Area")
     ],
+    outputs=[
+        gr.Text(label="Loan Status"),
+        gr.Text(label="Approval Probability"),
+        gr.Text(label="Risk Category")
+    ],
+    title="❄️ ICE — Intelligent Credit Evaluator",
+    description="Enter applicant details to predict loan approval status with confidence score and risk level."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()