Update app.py

app.py

@@ -81,14 +81,10 @@ PRODUCT_NAMES = [
     "Fusion", "Agroking", "CG commercial motors", "Jaguar", "Gaurav"
 ]
 
-# Salesforce credentials (use environment variables for security)
-SALESFORCE_USERNAME = os.getenv("SALESFORCE_USERNAME", "MISSING_USERNAME")
-SALESFORCE_PASSWORD = os.getenv("SALESFORCE_PASSWORD", "MISSING_PASSWORD")
-SALESFORCE_SECURITY_TOKEN = os.getenv("SALESFORCE_SECURITY_TOKEN", "MISSING_TOKEN")
-
-# Validate credentials
-if not SALESFORCE_USERNAME or not SALESFORCE_PASSWORD or not SALESFORCE_SECURITY_TOKEN:
-    raise ValueError("Salesforce credentials are missing. Please set the environment variables.")
+# Salesforce credentials
+SALESFORCE_USERNAME = "Venkatramana@sandbox.com"
+SALESFORCE_PASSWORD = "Seta12345@"
+SALESFORCE_SECURITY_TOKEN = "Drl0jchCwLBfvX4ODMeFDksP"
 
 # Initialize PaddleOCR
 ocr = PaddleOCR(use_angle_cls=True, lang='en')
@@ -126,19 +122,80 @@ def extract_attributes(extracted_text):
 
     return attributes
 
+# Function to interact with Salesforce
+def interact_with_salesforce(mode, entry_type, quantity, attributes):
+    try:
+        sf = Salesforce(
+            username=SALESFORCE_USERNAME,
+            password=SALESFORCE_PASSWORD,
+            security_token=SALESFORCE_SECURITY_TOKEN
+        )
+
+        # Define object and field names based on mode and entry type
+        if mode == "Entry":
+            if entry_type == "Sales":
+                object_name = "VENKATA_RAMANA_MOTORS__c"
+                field_name = "Quantity__c"
+            elif entry_type == "Non-Sales":
+                object_name = "UNBILLING_DATA__c"
+                field_name = "TotalQuantity__c"
+        elif mode == "Exit":
+            if entry_type == "Sales":
+                object_name = "Inventory_Management__c"
+                field_name = "Quantity_Sold__c"
+            elif entry_type == "Non-Sales":
+                object_name = "Un_Billable__c"
+                field_name = "Sold_Out__c"
+        else:
+            return "Invalid mode or entry type."
+
+        # Prepare data for Salesforce
+        sf_object = sf.__getattr__(object_name)
+        attributes[field_name] = quantity
+        sf_object.create(attributes)
+
+        return f"✅ Data successfully exported to Salesforce object {object_name}."
+    except Exception as e:
+        return f"❌ Error interacting with Salesforce: {str(e)}"
+
+# Function to process image, extract attributes, and allow editing
+def process_image(image, mode, entry_type, quantity):
+    extracted_text = extract_text(image)
+    if not extracted_text:
+        return "No text detected in the image.", None, None
+
+    product_name = match_product_name(extracted_text)
+    attributes = extract_attributes(extracted_text)
+    if product_name:
+        attributes["Product name"] = product_name
+
+    # Ensure fixed attributes are present
+    for fixed_attr in ["Stage", "H.P.", "Product name", "Model"]:
+        if fixed_attr not in attributes:
+            attributes[fixed_attr] = ""
+
+    # Convert attributes to DataFrame for editing
+    df = pd.DataFrame(list(attributes.items()), columns=["Attribute", "Value"])
+    return f"Extracted Text:\n{extracted_text}", df, None
+
 # Gradio Interface
 def app():
     with gr.Blocks() as demo:
         with gr.Tab("📥 OCR Processing"):
             with gr.Row():
                 image_input = gr.Image(type="numpy", label="📄 Upload Image")
-                extract_button = gr.Button("Extract Text")
-                extracted_text_output = gr.Text(label="📝 Extracted Image Data")
+                mode_input = gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry")
+                entry_type_input = gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales")
+                quantity_input = gr.Number(label="🔢 Quantity", value=1, interactive=True)
+            extract_button = gr.Button("Extract Text and Attributes")
+            extracted_text_output = gr.Text(label="📝 Extracted Image Data")
+            editable_df_output = gr.Dataframe(label="✏️ Edit Attributes (Key-Value Pairs)", interactive=True)
+            result_output = gr.Text(label="🚀 Result")
 
         extract_button.click(
-            fn=extract_text,
-            inputs=[image_input],
-            outputs=[extracted_text_output]
+            fn=process_image,
+            inputs=[image_input, mode_input, entry_type_input, quantity_input],
+            outputs=[extracted_text_output, editable_df_output, result_output]
         )
 
     return demo