Update app.py

app.py

@@ -15,88 +15,62 @@ 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')
+# 1. Initialize Client WITHOUT api_version
+# This specifically fixes the 'INVALID_ARGUMENT' error you received
+client = genai.Client(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]
-})
+# Use the direct model ID
+STABLE_MODEL = "gemini-1.5-flash"
 
+# --- Data Preparation ---
+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."
+# --- Upload Policies once on startup ---
+# These files MUST be in the same folder as app.py in Hugging Face
+risk_policy = client.files.upload(file="Bank Loan Overall Risk Policy.pdf")
+rate_policy = client.files.upload(file="Bank Loan Interest Rate Policy.pdf")
+
+def process_loan(applicant_id, optional_name):
+    try:
+        row = df_merged[df_merged['ID'] == int(applicant_id)]
+        if row.empty: return "ID not found."
+        applicant = row.iloc[0]
+
+        prompt = f"""
+        Assess this loan application using the provided PDF policies:
+        Name: {applicant['Name']} (ID: {applicant_id}), Score: {applicant['Credit_Score']}, Status: {applicant['Status']}
+        Nationality: {applicant['Nationality']}, PR Status: {applicant['PR_Status']}
+        
+        Follow the multi-step report format:
+        1. Info retrieval 2. PR check 3. Risk Assessment 4. Interest Rate 5. Recommendation
+        """
+
+        # Using direct file objects in the contents list
+        response = client.models.generate_content(
+            model=STABLE_MODEL,
+            contents=[risk_policy, rate_policy, prompt]
+        )
+        return response.text
+
+    except exceptions.ResourceExhausted:
+        return "Free tier limit reached. Please wait 60 seconds."
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # --- 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."
+    fn=process_loan,
+    inputs=[gr.Textbox(label="Applicant ID"), gr.Textbox(label="Name (Optional)")],
+    outputs=gr.Markdown(),
+    title="🏦 Enterprise Loan Underwriter"
 )
 
 if __name__ == "__main__":