code update in app.py

app.py

@@ -16,7 +16,6 @@ model = AutoModelForCausalLM.from_pretrained(
     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
     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 successfully!")
@@ -31,7 +30,7 @@ def parse_banking_sms(raw_text: str) -> dict:
 
     inputs = tokenizer(prompt, return_tensors="pt")
     if torch.cuda.is_available():
-        inputs = {k: v.to("cuda") for k, v in inputs.items()}
+        inputs = {k: v.cuda() for k, v in inputs.items()}
 
     with torch.no_grad():
         outputs = model.generate(
@@ -73,78 +72,58 @@ def parse_banking_sms(raw_text: str) -> dict:
 # --------------------------------------------------
 # 3. Chatbot response handler
 # --------------------------------------------------
-def chatbot_response(message, history):
-    """Handle user input and generate chatbot response"""
-    try:
-        result = parse_banking_sms(message)
-
-        if result["is_transaction"]:
-            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']}\n\n"
-                f"**Full JSON:**\n```json\n{json.dumps(result, indent=2)}\n```"
-            )
-        else:
-            response = (
-                "ℹ️ **Non-Transaction Message**\n\n"
-                "This appears to be a promotional or informational message, not a banking transaction.\n\n"
-                f"**Classification:**\n```json\n{json.dumps(result, indent=2)}\n```"
-            )
-    except Exception as e:
-        response = f"❌ **Error:** Sorry, I couldn't parse that message.\n\nError: {str(e)}"
+def chatbot_response(raw_text):
+    history = []
+    result = parse_banking_sms(raw_text)
+
+    if result["is_transaction"]:
+        response = (
+            "✅ **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']}\n\n"
+            "**Full JSON:**\n```
+            + json.dumps(result, indent=2)
+            + "\n```"
+        )
+    else:
+        response = (
+            "ℹ️ **Non-Transaction Message**\n\n"
+            "This appears to be a promotional or informational message, not a banking transaction.\n\n"
+            "**Classification:**\n```
+            + json.dumps(result, indent=2)
+            + "\n```"
+        )
 
-    history = history or []
-    history.append((message, response))
-    return history, history
+    history.append((raw_text, response))
+    return history
 
 # --------------------------------------------------
-# 4. Gradio interface
+# 4. Gradio Interface
 # --------------------------------------------------
-with gr.Blocks(theme=gr.themes.Soft(), title="🏦 Banking SMS JSON Parser") as demo:
-    gr.Markdown("""
-    # 🏦 Banking SMS JSON Parser Chatbot
-    
-    Paste any banking SMS/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)
-    msg = gr.Textbox(
-        label="Paste your banking SMS/email here",
-        placeholder="Example: Your A/c XX1234 debited for 5000 at AMAZON...",
-        lines=3
-    )
-    chat_history = gr.State([])
-
-    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"],
-            ["Transaction Alert: 45.99 debited from **2468 at NETFLIX"],
-            ["Your account balance is 5000. Thank you for banking with us."],
-            ["Congratulations! You are eligible for a personal loan up to 50000."]
-        ],
-        inputs=msg,
-        label="Try these example messages:"
-    )
-
-    msg.submit(chatbot_response, [msg, chat_history], [chatbot, chat_history])
-    msg.submit(lambda: "", None, msg)  # Clear input
-
-    gr.Markdown("---\n**Model:** `rawsun00001/banking-sms-json-parser-v6-merged`")
+iface = gr.Interface(
+    fn=chatbot_response,
+    inputs=gr.Textbox(
+        lines=3,
+        placeholder="Paste your banking SMS or email here…"
+    ),
+    outputs=gr.Chatbot(
+        label="Banking SMS Parser",
+        height=400
+    ),
+    title="🏦 Banking SMS JSON Parser Chatbot",
+    description="Simply paste any banking SMS or email snippet and get structured JSON!",
+    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"],
+    ],
+)
 
 # --------------------------------------------------
 # 5. App launcher
 # --------------------------------------------------
 if __name__ == "__main__":
-    demo.launch(share=True, server_name="0.0.0.0", server_port=7860)
-
+    iface.launch(server_name="0.0.0.0", server_port=7860)