Delete app.py.txt

app.py.txt

@@ -1,94 +0,0 @@
-import os
-import time
-import pandas as pd
-import gradio as gr
-from google import genai
-from google.genai import types
-from google.api_core import exceptions
-
-# 1. Access the API Key from Hugging Face Secrets
-api_key = os.environ.get('GOOGLE_API_KEY')
-
-# Initialize the Client
-# Note: Using v1 for stable API version
-client = genai.Client(
-    api_key=api_key,
-    http_options=types.HttpOptions(api_version='v1')
-)
-
-# Use gemini-1.5-flash for the best free-tier stability
-FREE_STABLE_MODEL = "gemini-1.5-flash"
-
-# --- Data Preparation (Internal Systems) ---
-df_credit = pd.DataFrame({
-    'ID': [1111, 2222, 3333, 4444, 5555],
-    'Credit_Score': [455, 685, 825, 840, 350]
-})
-
-df_account = pd.DataFrame({
-    'ID': [1111, 2222, 3333, 4444, 5555],
-    'Name': ['Loren', 'Matt', 'Hilda', 'Andy', 'Kit'],
-    'Status': ['good-standing', 'closed', 'delinquent', 'good-standing', 'delinquent'],
-    'Nationality': ['Singaporean', 'NonSingaporean', 'Singaporean', 'NonSingaporean', 'Singaporean']
-})
-
-df_gov = pd.DataFrame({'ID': [2222, 4444], 'PR_Status': [True, False]})
-df_merged = df_account.merge(df_credit, on="ID").merge(df_gov, on="ID", how="left").fillna(False)
-
-# --- Upload Policies to Files API ---
-# In Spaces, these files must be in the same folder as app.py
-risk_policy_file = client.files.upload(file="Bank Loan Overall Risk Policy.pdf")
-interest_policy_file = client.files.upload(file="Bank Loan Interest Rate Policy.pdf")
-
-# --- Assessment Logic ---
-def process_loan_request(applicant_id, customer_name_optional, max_retries=3):
-    for attempt in range(max_retries):
-        try:
-            row = df_merged[df_merged['ID'] == int(applicant_id)]
-            if row.empty: return "Error: Applicant ID not found in system."
-            applicant = row.iloc[0]
-
-            prompt = f"""
-            You are a Senior Loan Officer. Assess this application using the provided PDF policies.
-
-            APPLICANT DATA:
-            Name: {applicant['Name']} (ID: {applicant_id})
-            Credit Score: {applicant['Credit_Score']}
-            Account Status: {applicant['Status']}
-            Nationality: {applicant['Nationality']}
-            PR Status: {applicant['PR_Status']}
-
-            REQUIRED STEPS:
-            Step 1. Retrieve Information: State the name, score, status, and nationality.
-            Step 2. PR Status Check: Mandatory for Non-Singaporeans.
-            Step 3. Check Overall Risk: Use the 'Bank Loan Overall Risk Policy' table.
-            Step 4. Check Interest Rate: Use the 'Bank Loan Interest Rate Policy' table.
-            Step 5. Report: Recommend only if Singaporean OR (Non-Singaporean AND PR Status is True).
-            """
-
-            response = client.models.generate_content(
-                model=FREE_STABLE_MODEL,
-                contents=[risk_policy_file, interest_policy_file, prompt]
-            )
-            return response.text
-
-        except exceptions.ResourceExhausted:
-            wait_time = (2 ** attempt) + 5
-            time.sleep(wait_time)
-
-    return "Error: System currently at max capacity. Please retry in 1 minute."
-
-# --- Gradio Interface ---
-demo = gr.Interface(
-    fn=process_loan_request,
-    inputs=[
-        gr.Textbox(label="Enter Applicant ID"),
-        gr.Textbox(label="Enter Customer Name (Optional)")
-    ],
-    outputs=gr.Markdown(label="Underwriter Assessment Report"),
-    title="🏦 Enterprise AI Loan Underwriter",
-    description="Automated risk assessment via multi-system data integration and PDF policy compliance."
-)
-
-if __name__ == "__main__":
-    demo.launch()