code update in app.py

app.py

@@ -3,14 +3,10 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import json
 import re
-from fastapi import FastAPI
-import uvicorn
 
 MODEL_ID = "rawsun00001/banking-sms-json-parser-v6-merged"
 
-# -------------------------------
-# 1. Load model & tokenizer
-# -------------------------------
+print("🔄 Loading banking‑SMS JSON parser model...")
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
@@ -19,11 +15,11 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 if tokenizer.pad_token is None:
     tokenizer.pad_token = tokenizer.eos_token
+print("✅ Model loaded successfully!")
 
-
-def parse_banking_sms(raw_text):
-    sms = " ".join(raw_text.split())
-    prompt = sms + "|"
+def parse_banking_sms(raw_text: str) -> dict:
+    sms_text = " ".join(raw_text.strip().split())
+    prompt = sms_text + "|"
     inputs = tokenizer(prompt, return_tensors="pt")
     if torch.cuda.is_available():
         inputs = {k: v.cuda() for k, v in inputs.items()}
@@ -34,14 +30,14 @@ def parse_banking_sms(raw_text):
             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()
-    match = re.search(r"\{[^{}]+\}", json_part)
-    if match:
+    m = re.search(r"\{[^{}]+\}", json_part)
+    if m:
         try:
-            parsed = json.loads(match.group())
+            parsed = json.loads(m.group())
             return {
                 "date": parsed.get("date"),
                 "type": parsed.get("type"),
@@ -53,68 +49,48 @@ def parse_banking_sms(raw_text):
         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_msg, history):
-    # 🛠 Fix: history must always be a list (Gradio v5 validation expects list of dicts)
-    if not isinstance(history, list):
-        history = []
-
-    res = parse_banking_sms(user_msg)
-    if res["is_transaction"]:
-        assistant_msg = (
+def predict(raw_text: str) -> str:
+    parsed = parse_banking_sms(raw_text)
+    if parsed["is_transaction"]:
+        summary = (
             f"✅ Transaction Detected!\n\n"
-            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```"
+            f"- 📅 Date: **{parsed.get('date', 'N/A')}**\n"
+            f"- 💳 Type: **{parsed.get('type', '').title()}**\n"
+            f"- 💰 Amount: **{parsed.get('amount')}**\n"
+            f"- 🏪 Category: **{parsed.get('category')}**\n"
+            f"- 🔢 Last 4 Digits: **{parsed.get('last4')}**\n\n"
+            "**Full Parsed JSON:**\n```json\n"
+            f"{json.dumps(parsed, indent=2)}\n```"
         )
     else:
-        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```"
+        summary = (
+            "ℹ️ Non‑transactional SMS / Promotional / Info message.\n\n"
+            "**Parsed Classification JSON:**\n```json\n"
+            f"{json.dumps(parsed, 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
-
-    Paste your banking SMS or email below. The bot will extract structured transaction JSON.
-    """)
-
-    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]
-    )
-    msg_txt.submit(lambda _: "", None, msg_txt)
-
-    gr.Markdown(f"---\n**Model ID:** `{MODEL_ID}`")
-
-# -------------------------------
-# 5. Mount with FastAPI
-# -------------------------------
-app = FastAPI()
-app.mount("/", demo)
+    return summary
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(
+        lines=3,
+        placeholder="Paste your banking SMS or email here…",
+        label="Input SMS / Email"
+    ),
+    outputs=gr.Markdown(label="Parsed Output"),
+    title="🏦 Banking SMS JSON Parser",
+    description=(
+        "Paste any banking SMS (or email) below — the app will detect transaction "
+        "vs non-transaction, and display structured JSON output."
+    ),
+    allow_flagging="never",
+    analytics_enabled=False,  # disable Gradio telemetry in Spaces
+)
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    iface.launch(server_name="0.0.0.0", server_port=7860)