Update app.py

app.py

@@ -41,11 +41,18 @@ def normalize(box, width, height):
     ]
 
 # =====================================================
-# EXTRACT DATA
+# CONFIDENCE HELPER
+# =====================================================
+def avg_conf(lst):
+    if len(lst) == 0:
+        return 0
+    return sum(lst) / len(lst)
+
+# =====================================================
+# EXTRACT DATA + CONFIDENCE
 # =====================================================
 def extract_receipt(image):
     try:
-        # 🔥 Speed optimization
         image = image.convert("RGB")
         image.thumbnail((1200, 1200))
 
@@ -54,8 +61,7 @@ def extract_receipt(image):
             output_type=pytesseract.Output.DICT
         )
 
-        words = []
-        boxes = []
+        words, boxes = [], []
 
         for i in range(len(data["text"])):
             text = data["text"][i].strip()
@@ -90,7 +96,10 @@ def extract_receipt(image):
         with torch.no_grad():
             outputs = model(**encoding)
 
-        predictions = torch.argmax(outputs.logits, dim=2)[0][:len(words)]
+        # 🔥 CONFIDENCE LOGIC
+        probs = torch.softmax(outputs.logits, dim=2)
+        preds = torch.argmax(probs, dim=2)[0][:len(words)]
+        confs = torch.max(probs, dim=2)[0][0][:len(words)]
 
         result = {
             "company": [],
@@ -98,39 +107,70 @@ def extract_receipt(image):
             "total": []
         }
 
-        for word, pred in zip(words, predictions):
+        conf_store = {
+            "company": [],
+            "date": [],
+            "total": []
+        }
+
+        for word, pred, conf in zip(words, preds, confs):
             label = id2label[pred.item()]
+            confidence = conf.item()
 
             if label == "COMPANY":
                 result["company"].append(word)
+                conf_store["company"].append(confidence)
 
             if re.search(r"\d{2}[/-]\d{2}[/-]\d{2,4}", word):
                 result["date"].append(word)
+                conf_store["date"].append(confidence)
 
             if re.search(r"\d+(\.\d{2})?", word):
                 try:
                     value = float(word.replace(",", ""))
                     if value > 50:
                         result["total"].append(word)
+                        conf_store["total"].append(confidence)
                 except:
                     pass
 
+        # FINAL VALUES
         result["company"] = " ".join(result["company"]) if result["company"] else "Not Found"
         result["date"] = result["date"][0] if result["date"] else "Not Found"
         result["total"] = result["total"][-1] if result["total"] else "Not Found"
 
+        # 🔥 FINAL CONFIDENCE
+        company_conf = avg_conf(conf_store["company"])
+        date_conf = avg_conf(conf_store["date"])
+        total_conf = avg_conf(conf_store["total"])
+
+        overall_conf = (company_conf + date_conf + total_conf) / 3
+
+        result["confidence"] = round(overall_conf, 3)
+
         return result
 
     except Exception as e:
         return {"error": str(e)}
 
 # =====================================================
-# SEND EMAIL (RESEND API - WORKING)
+# DECISION ENGINE
+# =====================================================
+def decision_layer(confidence):
+    if confidence > 0.9:
+        return "AUTO_SEND"
+    elif confidence > 0.7:
+        return "REVIEW"
+    else:
+        return "REJECT"
+
+# =====================================================
+# SEND EMAIL (RESEND)
 # =====================================================
 def send_claim_email(to_email, extracted):
 
     if not RESEND_API_KEY:
-        return "❌ RESEND_API_KEY missing in HuggingFace Secrets"
+        return "❌ RESEND_API_KEY missing"
 
     subject = "Insurance Claim Request"
 
@@ -139,7 +179,6 @@ def send_claim_email(to_email, extracted):
     <p><b>Provider:</b> {extracted['company']}</p>
     <p><b>Date:</b> {extracted['date']}</p>
     <p><b>Amount:</b> ₹{extracted['total']}</p>
-    <p>Please process this claim.</p>
     """
 
     try:
@@ -150,7 +189,7 @@ def send_claim_email(to_email, extracted):
                 "Content-Type": "application/json",
             },
             json={
-                "from": "onboarding@resend.dev",
+                "from": "onboarding@resend.dev",  # change after domain verify
                 "to": [to_email],
                 "subject": subject,
                 "html": html_body,
@@ -158,10 +197,8 @@ def send_claim_email(to_email, extracted):
             timeout=10
         )
 
-        print("EMAIL RESPONSE:", response.status_code, response.text)
-
         if response.status_code in [200, 201]:
-            return f"✅ Email sent successfully to {to_email}"
+            return f"✅ Email sent to {to_email}"
         else:
             return f"❌ Email failed: {response.text}"
 
@@ -169,23 +206,29 @@ def send_claim_email(to_email, extracted):
         return f"❌ Email error: {str(e)}"
 
 # =====================================================
-# MAIN FUNCTION
+# MAIN PIPELINE
 # =====================================================
 def process_and_send(image, email_id):
 
-    print("Processing started...")
-
     extracted = extract_receipt(image)
-    print("Extracted:", extracted)
 
     if "error" in extracted:
         return extracted, extracted["error"]
 
-    print("Sending email to:", email_id)
+    confidence = extracted.get("confidence", 0)
+    decision = decision_layer(confidence)
+
+    # 🔥 DECISION CONTROL
+    if decision == "AUTO_SEND":
+        email_status = send_claim_email(email_id, extracted)
+
+    elif decision == "REVIEW":
+        email_status = f"⚠️ Needs human review (confidence={confidence})"
 
-    email_status = send_claim_email(email_id, extracted)
+    else:
+        email_status = f"❌ Rejected due to low confidence ({confidence})"
 
-    print("Email status:", email_status)
+    extracted["decision"] = decision
 
     return extracted, email_status
 
@@ -199,11 +242,11 @@ demo = gr.Interface(
         gr.Textbox(label="Enter Email ID")
     ],
     outputs=[
-        gr.JSON(label="Extracted Data"),
+        gr.JSON(label="Extracted Data + Confidence + Decision"),
         gr.Textbox(label="Email Status")
     ],
-    title="📄 AI Insurance Claim Generator",
-    description="Upload receipt → Extract details → Auto send claim email"
+    title="📄 AI Insurance Claim Generator (Enterprise Version)",
+    description="Upload receipt → AI extracts → Confidence check → Safe decision → Email"
 )
 
 demo.launch()