Update app.py

app.py

@@ -1,5 +1,5 @@
 import os
-from paddleocr import PaddleOCR, draw_ocr
+from paddleocr import PaddleOCR
 from PIL import Image
 import gradio as gr
 import re
@@ -71,69 +71,71 @@ def extract_attributes(extracted_text):
             attributes[readable_attr] = match.group(1).strip()
     return attributes
 
-# Function to fetch and return Salesforce data
-def fetch_salesforce_data():
+# Function to export extracted data to Salesforce
+def export_to_salesforce(mode, entry_type, quantity, extracted_text):
     try:
         sf = Salesforce(
             username=SALESFORCE_USERNAME,
             password=SALESFORCE_PASSWORD,
-            security_token=SALESFORCE_SECURITY_TOKEN,
-            domain='login'
+            security_token=SALESFORCE_SECURITY_TOKEN
         )
 
-        # SOQL Query
-        soql_query = "SELECT Product_Name__c, Modal_Name__c, Current_Stocks__c FROM Inventory_Management__c LIMIT 100"
-        response = sf.query(soql_query)
-        records = response.get('records', [])
+        # Determine Object & Field Mapping
+        object_name, field_name = None, None
 
-        if not records:
-            return None, None
+        if mode == "Entry":
+            object_name = "Inventory_Management__c" if entry_type == "Sales" else "Un_Billable__c"
+            field_name = "Quantity__c"
+        elif mode == "Exit":
+            object_name = "Inventory_Management__c" if entry_type == "Sales" else "Un_Billable__c"
+            field_name = "Quantity_Sold__c"
 
-        df = pd.DataFrame(records)
-        df.rename(columns={
-            'Product_Name__c': 'Product Name',
-            'Modal_Name__c': 'Model Name',
-            'Current_Stocks__c': 'Current Stocks'
-        }, inplace=True)
-        df.drop(columns=['attributes'], inplace=True, errors='ignore')
+        if not object_name or not field_name:
+            return "❌ Invalid mode or entry type."
 
-        return df, generate_bar_graph(df)
+        # Extract Product Name & Attributes
+        product_name = match_product_name(extracted_text)
+        attributes = extract_attributes(extracted_text)
 
-    except Exception as e:
-        return None, None
+        if not product_name:
+            return "❌ No matching product found."
 
-# Function to generate a bar graph from Salesforce data
-def generate_bar_graph(df):
-    try:
-        fig, ax = plt.subplots(figsize=(12, 8))
-        df.plot(kind='bar', x="Product Name", y="Current Stocks", ax=ax, legend=False)
-        ax.set_title("Stock Distribution by Product Name")
-        ax.set_xlabel("Product Name")
-        ax.set_ylabel("Current Stocks")
-        plt.xticks(rotation=45, ha="right", fontsize=10)
-        plt.tight_layout()
-        buffer = BytesIO()
-        plt.savefig(buffer, format="png")
-        buffer.seek(0)
-        return Image.open(buffer)
-    except Exception as e:
-        return None
+        attributes["Product name"] = product_name
+
+        # Validate Salesforce fields
+        sf_object = sf.__getattr__(object_name)
+        schema = sf_object.describe()
+        valid_fields = {field["name"] for field in schema["fields"]}
+
+        filtered_attributes = {ATTRIBUTE_MAPPING[key]: value for key, value in attributes.items() if ATTRIBUTE_MAPPING[key] in valid_fields}
+        filtered_attributes[field_name] = quantity
 
-# Function to generate Salesforce data table and graph
-def generate_salesforce_data():
-    df, graph = fetch_salesforce_data()
-    if df is not None:
-        return df.to_html(index=False), graph
-    return "<p>No Data Available</p>", None
+        # Check if record exists
+        query = f"SELECT Id FROM {object_name} WHERE Product_Name__c = '{product_name}' LIMIT 1"
+        response = sf.query(query)
 
-# Function to process images and export to Salesforce
+        if response["records"]:
+            record_id = response["records"][0]["Id"]
+            sf_object.update(record_id, filtered_attributes)
+            return f"✅ Updated record for '{product_name}' in {object_name}."
+        else:
+            sf_object.create(filtered_attributes)
+            return f"✅ Created new record in {object_name} for '{product_name}'."
+
+    except Exception as e:
+        return f"❌ Error exporting data to Salesforce: {str(e)}"
+
+# Function to process images, extract attributes, and export to Salesforce
 def process_image(image, mode, entry_type, quantity):
     extracted_text = extract_text(image)
     if "OCR Error" in extracted_text:
         return extracted_text, None
 
-    result = f"📜 Extracted Text:\n{extracted_text}"
-    return result, "✅ Successfully processed image."
+    attributes = extract_attributes(extracted_text)
+    attributes_list = "\n".join([f"{key}: {value}" for key, value in attributes.items()])
+
+    result = export_to_salesforce(mode, entry_type, quantity, extracted_text)
+    return f"📜 Extracted Attributes:\n{attributes_list}", result
 
 # Gradio App
 def app():
@@ -149,17 +151,11 @@ def app():
                     quantity_input = gr.Number(label="🔢 Quantity", value=1, interactive=True)
 
                 with gr.Column():
-                    image_view = gr.Text(label="📜 Extracted Data", interactive=False)
-                    result_output = gr.Text(label="📝 Processing Result", interactive=False)
-                    submit_button = gr.Button("🔍 Process Image")
-
-        with gr.Tab("📊 Salesforce Data Overview"):
-            salesforce_table = gr.HTML(label="📦 Salesforce Data Table")
-            salesforce_graph = gr.Image(type="pil", label="📉 Stock Distribution Graph")
-            generate_button = gr.Button("⚡ Generate Data")
+                    image_view = gr.Text(label="📜 Extracted Attributes", interactive=False)
+                    result_output = gr.Text(label="📝 Salesforce Export Result", interactive=False)
+                    submit_button = gr.Button("🔍 Process & Export")
 
         # Button Click Events
-        generate_button.click(fn=generate_salesforce_data, inputs=[], outputs=[salesforce_table, salesforce_graph])
         submit_button.click(fn=process_image, inputs=[image_input, mode_dropdown, entry_type_radio, quantity_input], outputs=[image_view, result_output])
 
     return interface