code update in app.py

app.py

@@ -6,12 +6,9 @@ import re
 from fastapi import FastAPI
 import uvicorn
 
-# --------------------------------------------------
-# 1. Model loading
-# --------------------------------------------------
 MODEL_ID = "rawsun00001/banking-sms-json-parser-v6-merged"
 
-print("🔄 Loading banking SMS JSON parser model...")
+print("🔄 Loading banking‑SMS JSON parser model...")
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
@@ -20,19 +17,14 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 if tokenizer.pad_token is None:
     tokenizer.pad_token = tokenizer.eos_token
-print("✅ Model loaded successfully!")
+print("✅ Model loaded")
 
-# --------------------------------------------------
-# 2. Core parsing function
-# --------------------------------------------------
 def parse_banking_sms(raw_text: str) -> dict:
-    sms_text = " ".join(raw_text.strip().split())
-    prompt = f"{sms_text}|"
-
+    sms = " ".join(raw_text.strip().split())
+    prompt = sms + "|"
     inputs = tokenizer(prompt, return_tensors="pt")
     if torch.cuda.is_available():
         inputs = {k: v.cuda() for k, v in inputs.items()}
-
     with torch.no_grad():
         outputs = model.generate(
             **inputs,
@@ -42,14 +34,12 @@ def parse_banking_sms(raw_text: str) -> dict:
             pad_token_id=tokenizer.eos_token_id,
             eos_token_id=tokenizer.eos_token_id,
         )
-
     decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    json_part = decoded[len(prompt):].strip()
-
-    json_match = re.search(r'\{[^{}]+\}', json_part)
-    if json_match:
+    json_part = decoded[len(prompt) :].strip()
+    m = re.search(r"\{[^{}]+\}", json_part)
+    if m:
         try:
-            parsed = json.loads(json_match.group())
+            parsed = json.loads(m.group())
             return {
                 "date": parsed.get("date"),
                 "type": parsed.get("type"),
@@ -60,72 +50,49 @@ def parse_banking_sms(raw_text: str) -> dict:
             }
         except json.JSONDecodeError:
             pass
-
     return {
         "date": None,
         "type": None,
         "amount": None,
         "category": None,
         "last4": None,
-        "is_transaction": False
+        "is_transaction": False,
     }
 
-# --------------------------------------------------
-# 3. Chatbot response handler
-# --------------------------------------------------
-def chatbot_response(raw_text, chat_history):
-    result = parse_banking_sms(raw_text)
+def chatbot_response(user_message, history):
+    history = history or []
+    result = parse_banking_sms(user_message)
     if result["is_transaction"]:
-        response = f"""✅ Transaction Detected!
-
-📅 Date: {result['date']}
-💳 Type: {result['type'].title() if result['type'] else 'N/A'}
-💰 Amount: {result['amount']}
-🏪 Category: {result['category']}
-🔢 Last 4 Digits: {result['last4']}"""
+        response = (
+            f"✅ Transaction Detected!\n\n"
+            f"📅 Date: {result['date']}\n"
+            f"💳 Type: {result['type'].title() if result['type'] else 'N/A'}\n"
+            f"💰 Amount: {result['amount']}\n"
+            f"🏪 Category: {result['category']}\n"
+            f"🔢 Last 4 Digits: {result['last4']}"
+        )
     else:
-        response = "ℹ️ Non-Transaction Message\n\nThis appears to be a promotional or informational message."
-
-    chat_history = chat_history or []
-    chat_history.append({"role": "user", "content": raw_text})
-    chat_history.append({"role": "assistant", "content": response})
-    return chat_history, ""
+        response = ("ℹ️ Non-Transaction Message\n\n"
+                    "This looks like a promo or info message.")
+    history.append({"role": "user", "content": user_message})
+    history.append({"role": "assistant", "content": response})
+    return history, ""
 
-# --------------------------------------------------
-# 4. Gradio Blocks UI (Spaces-compatible)
-# --------------------------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("""
-    # 🏦 Banking SMS JSON Parser Chatbot
-
-    Paste any banking SMS or email below – no special formatting needed!
-
-    **Features:**
-    - ✅ Detects real transactions vs promotional messages
-    - ✅ Extracts date, amount, merchant, category, account info
-    - ✅ Works with all Indian & global banking formats
-    """)
-
-    chatbot = gr.Chatbot(label="Banking SMS Parser", height=400, type="messages")
-    msg = gr.Textbox(lines=3, placeholder="Paste your banking SMS/email here…", label="Input Message")
-
-    gr.Examples(
-    examples=[
-        "Your A/c XX1234 debited for 5000 on 15-Jan-2024 at AMAZON",
-        "2500 credited to A/c 9876 on 20-Dec-2023 from PAYROLL",
-        "Card 4321 used for 120 at STARBUCKS on 10-Nov-2023",
-    ],
-    inputs=msg
-)
-
+    gr.Markdown(
+        """
+        ## 🏦 Banking SMS JSON Parser Chatbot
+
+        Paste your banking SMS here — the app will detect whether it's a transaction
+        and extract date, amount, merchant, etc.
+        """
+    )
+    chatbot = gr.Chatbot(type="messages", label="Parser Bot", height=400)
+    msg = gr.Textbox(lines=3, label="Your SMS message")
     msg.submit(chatbot_response, inputs=[msg, chatbot], outputs=[chatbot, msg])
     msg.submit(lambda: "", None, msg)
+    gr.Markdown(f"\n---\n**Backend model:** `{MODEL_ID}`")
 
-    gr.Markdown("---\n**Model:** `rawsun00001/banking-sms-json-parser-v6-merged`")
-
-# --------------------------------------------------
-# 5. App launcher
-# --------------------------------------------------
 app = FastAPI()
 app.mount("/", demo)