code update in app.py

app.py

@@ -8,19 +8,21 @@ import uvicorn
 
 MODEL_ID = "rawsun00001/banking-sms-json-parser-v6-merged"
 
-print("🔄 Loading banking‑SMS JSON parser model...")
+# -------------------------------
+# 1. Load model & tokenizer
+# -------------------------------
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto" if torch.cuda.is_available() else None,
+    device_map="auto" if torch.cuda.is_available() else None
 )
 if tokenizer.pad_token is None:
     tokenizer.pad_token = tokenizer.eos_token
-print("✅ Model loaded")
 
-def parse_banking_sms(raw_text: str) -> dict:
-    sms = " ".join(raw_text.strip().split())
+
+def parse_banking_sms(raw_text):
+    sms = " ".join(raw_text.split())
     prompt = sms + "|"
     inputs = tokenizer(prompt, return_tensors="pt")
     if torch.cuda.is_available():
@@ -32,14 +34,14 @@ def parse_banking_sms(raw_text: str) -> dict:
             do_sample=False,
             repetition_penalty=1.05,
             pad_token_id=tokenizer.eos_token_id,
-            eos_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()
-    m = re.search(r"\{[^{}]+\}", json_part)
-    if m:
+    match = re.search(r"\{[^{}]+\}", json_part)
+    if match:
         try:
-            parsed = json.loads(m.group())
+            parsed = json.loads(match.group())
             return {
                 "date": parsed.get("date"),
                 "type": parsed.get("type"),
@@ -51,52 +53,66 @@ 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,
+        "date": None, "type": None, "amount": None,
+        "category": None, "last4": None, "is_transaction": False
     }
 
-def chatbot_response(user_message, history):
-    # Check if history is a list, else initialize empty list
+
+def chatbot_response(user_msg, history):
+    # 🛠 Fix: history must always be a list (Gradio v5 validation expects list of dicts)
     if not isinstance(history, list):
         history = []
-    
-    result = parse_banking_sms(user_message)
-    if result["is_transaction"]:
-        response = (
+
+    res = parse_banking_sms(user_msg)
+    if res["is_transaction"]:
+        assistant_msg = (
             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']}"
+            f"📅 Date: {res['date']}\n"
+            f"💳 Type: {res['type'].title() if res['type'] else 'N/A'}\n"
+            f"💰 Amount: {res['amount']}\n"
+            f"🏪 Category: {res['category']}\n"
+            f"🔢 Last 4 Digits: {res['last4']}\n\n"
+            f"**Full JSON:**\n```json\n{json.dumps(res, indent=2)}\n```"
         )
     else:
-        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})
+        assistant_msg = (
+            "ℹ️ Non-Transaction Message\n\n"
+            "This appears to be a promotional or informational SMS or email.\n\n"
+            f"**Classification JSON:**\n```json\n{json.dumps(res, indent=2)}\n```"
+        )
+
+    # Update history
+    history.append({"role": "user", "content": user_msg})
+    history.append({"role": "assistant", "content": assistant_msg})
+
     return history, ""
 
+
+# -------------------------------
+# 4. Gradio UI
+# -------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        ## 🏦 Banking SMS JSON Parser Chatbot
+    gr.Markdown("""
+    ## 🏦 Banking SMS JSON Parser
+
+    Paste your banking SMS or email below. The bot will extract structured transaction JSON.
+    """)
 
-        Paste your banking SMS here — the app will detect whether it's a transaction
-        and extract date, amount, merchant, etc.
-        """
+    chatbot_ui = gr.Chatbot(type="messages", value=[])
+    msg_txt = gr.Textbox(lines=3, placeholder="Paste SMS here…", label="Input SMS")
+
+    msg_txt.submit(
+        fn=chatbot_response,
+        inputs=[msg_txt, chatbot_ui],
+        outputs=[chatbot_ui, msg_txt]
     )
-    chatbot = gr.Chatbot(type="messages", label="Parser Bot", height=400, value=[])  # initial empty list
-    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}`")
+    msg_txt.submit(lambda _: "", None, msg_txt)
+
+    gr.Markdown(f"---\n**Model ID:** `{MODEL_ID}`")
 
+# -------------------------------
+# 5. Mount with FastAPI
+# -------------------------------
 app = FastAPI()
 app.mount("/", demo)