Update app.py

app.py

@@ -78,7 +78,7 @@ ATTRIBUTE_MAPPING = {
 
 # List of product names to match
 PRODUCT_NAMES = [
-     "Fusion", "Agroking", "openwell", "CG commercial motors", "Jaguar", "Submersible pumps", "Gaurav"
+    "Fusion", "Agroking", "openwell", "CG commercial motors", "Jaguar", "Submersible pumps", "Gaurav"
 ]
 
 # Salesforce credentials
@@ -98,7 +98,7 @@ def extract_text(image):
     return "\n".join(extracted_text)
 
 # Function to match product name using fuzzy matching
-def match_product_name(extracted_text, threshold=70):
+def match_product_name(extracted_text):
     best_match = None
     best_score = 0
 
@@ -108,7 +108,7 @@ def match_product_name(extracted_text, threshold=70):
             best_match = match
             best_score = score
 
-    return best_match if best_score >= threshold else None  # Adjustable threshold for a match
+    return best_match if best_score >= 70 else None  # Threshold of 70 for a match
 
 # Function to extract attributes and their values
 def extract_attributes(extracted_text):
@@ -167,25 +167,19 @@ def interact_with_salesforce(mode, entry_type, quantity, attributes):
         schema = sf_object.describe()
         valid_fields = {field["name"] for field in schema["fields"]}
 
-        # Extract product name and attributes
-        product_name = attributes.get("Product name", "").strip()
-        model_name = attributes.get("Model Name", "").strip()
-        stage = attributes.get("Stage", "").strip()
-        hp = attributes.get("H.P.", "").strip()
-
-        if not product_name:
-            return "Product name could not be matched from the extracted text."
+        # Filter attributes for valid Salesforce fields
+        filtered_attributes = filter_valid_attributes(attributes, valid_fields)
 
         # Handling "Exit" Mode (Updating Records)
         if mode == "Exit":
             # Query should match exact product name, model name, stage, and hp if available
-            query_conditions = [f"{product_field_name} = '{product_name}'"]
-            if model_name:
-                query_conditions.append(f"{model_field_name} = '{model_name}'")
-            if stage:
-                query_conditions.append(f"{stage_field_name} = '{stage}'")
-            if hp:
-                query_conditions.append(f"{hp_field_name} = '{hp}'")
+            query_conditions = [f"{product_field_name} = '{attributes['Product name']}'"]
+            if "Model Name" in attributes:
+                query_conditions.append(f"{model_field_name} = '{attributes['Model Name']}'")
+            if "Stage" in attributes:
+                query_conditions.append(f"{stage_field_name} = '{attributes['Stage']}'")
+            if "H.P." in attributes:
+                query_conditions.append(f"{hp_field_name} = '{attributes['H.P.']}'")
 
             query = f"SELECT Id, {', '.join(field_names)} FROM {object_name} WHERE {' AND '.join(query_conditions)} LIMIT 1"
             response = sf.query(query)
@@ -194,23 +188,12 @@ def interact_with_salesforce(mode, entry_type, quantity, attributes):
                 record_id = response["records"][0]["Id"]
                 updated_fields = {field: quantity for field in field_names}
                 sf_object.update(record_id, updated_fields)
-                return f"✅ Updated record for product '{product_name}' ({model_name}) in {object_name}. Updated fields: {updated_fields}."
+                return f"✅ Updated record for product '{attributes['Product name']}' in {object_name}. Updated fields: {updated_fields}."
             else:
-                # If no matching record found with all conditions, try with only product name
-                query_conditions = [f"{product_field_name} = '{product_name}'"]
-                query = f"SELECT Id, {', '.join(field_names)} FROM {object_name} WHERE {' AND '.join(query_conditions)} LIMIT 1"
-                response = sf.query(query)
-                if response["records"]:
-                    record_id = response["records"][0]["Id"]
-                    updated_fields = {field: quantity for field in field_names}
-                    sf_object.update(record_id, updated_fields)
-                    return f"✅ Updated record for product '{product_name}' in {object_name}. Updated fields: {updated_fields}."
-                else:
-                    return f"❌ No matching record found for product '{product_name}' in {object_name}."
+                return f"❌ No matching record found for product '{attributes['Product name']}' in {object_name}."
 
         # Handling "Entry" Mode (Creating Records)
         else:
-            filtered_attributes = filter_valid_attributes(attributes, valid_fields)
             filtered_attributes[field_name] = quantity
             sf_object.create(filtered_attributes)
             return f"✅ Data successfully exported to Salesforce object {object_name}."
@@ -218,129 +201,67 @@ def interact_with_salesforce(mode, entry_type, quantity, attributes):
     except Exception as e:
         return f"❌ Error interacting with Salesforce: {str(e)}"
 
-# Function to pull structured data from Salesforce and display as a table
-def pull_data_from_salesforce(selected_object):
-    try:
-        sf = Salesforce(
-            username=SALESFORCE_USERNAME,
-            password=SALESFORCE_PASSWORD,
-            security_token=SALESFORCE_SECURITY_TOKEN
-        )
-        
-        if selected_object == "Inventory_Management__c":
-            query = "SELECT Productname__c, Current_Stocks__c, soldstock__c FROM Inventory_Management__c LIMIT 100"
-        elif selected_object == "Un_Billable__c":
-            query = "SELECT Productname__c, Current_Stock__c, soldstock__c FROM Un_Billable__c LIMIT 100"
-        else:
-            return "Invalid object selected.", None, None, None
-        
-        response = sf.query_all(query)
-        records = response.get("records", [])
-        
-        if not records:
-            return "No data found in Salesforce.", None, None, None
-        
-        df = pd.DataFrame(records)
-        df = df.drop(columns=['attributes'], errors='ignore')
-       
-        # Rename columns for better readability
-        df.rename(columns={
-            "Productname__c": "Product Name",
-            "Current_Stocks__c": "Current Stocks",
-            "Current_Stock__c": "Current Stocks",
-            "soldstock__c": "Sold Stock"
-        }, inplace=True)
-       
-        excel_path = "salesforce_data.xlsx"
-        df.to_excel(excel_path, index=False)
-       
-        # Generate interactive vertical bar graph using Matplotlib
-        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)
-        img = Image.open(buffer)
-       
-        return "Data successfully retrieved.", df, excel_path, img
-    except Exception as e:
-        return f"Error fetching data: {str(e)}", None, None, None
-
-# Unified function to handle image processing and Salesforce interaction
-def process_image(image, mode, entry_type, quantity, threshold=70):
+# 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, threshold)
+    product_name = match_product_name(extracted_text)
     attributes = extract_attributes(extracted_text)
     if product_name:
         attributes["Product name"] = product_name
-    else:
-        attributes["Product name"] = "Unknown"
 
-    # Combine extracted text and attributes for display
-    combined_text = extracted_text + "\n\nExtracted Attributes:\n"
-    for key, value in attributes.items():
-        combined_text += f"{key}: {value}\n"
+    # Convert attributes to DataFrame for editing
+    df = pd.DataFrame(list(attributes.items()), columns=["Attribute", "Value"])
+    return f"Extracted Text:\n{extracted_text}", df, None
 
-    return combined_text, attributes
-
-# Function to export edited attributes to Salesforce
-def export_to_salesforce(mode, entry_type, quantity, attributes):
-    message = interact_with_salesforce(mode, entry_type, quantity, attributes)
-    return message
+# Function to handle edited attributes and export to Salesforce
+def export_to_salesforce(mode, entry_type, quantity, edited_df):
+    try:
+        # Convert edited DataFrame back to dictionary
+        edited_attributes = dict(zip(edited_df["Attribute"], edited_df["Value"]))
+        
+        # Export to Salesforce
+        message = interact_with_salesforce(mode, entry_type, quantity, edited_attributes)
+        return message
+    except Exception as e:
+        return f"❌ Error exporting to Salesforce: {str(e)}"
 
 # Gradio Interface
 def app():
-    with gr.Blocks() as demo:
-        with gr.Tab("📥 OCR Processing"):
-            image_input = gr.Image(type="numpy", label="📄 Upload Image")
-            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)
-            threshold_input = gr.Slider(label="🔍 Matching Threshold", minimum=0, maximum=100, value=70, step=1)
-            extracted_text_output = gr.Text(label="📝 Extracted Image Data")
-            attributes_output = gr.Dataframe(label="✏️ Edit Attributes", headers=["Attribute", "Value"], interactive=True)
-            process_button = gr.Button("Process Image")
-
-            def process_image_and_display(image, mode, entry_type, quantity, threshold):
-                combined_text, attributes = process_image(image, mode, entry_type, quantity, threshold)
-                attributes_list = [[key, value] for key, value in attributes.items()]
-                return combined_text, attributes_list
-
-            process_button.click(process_image_and_display, inputs=[image_input, mode_input, entry_type_input, quantity_input, threshold_input], outputs=[extracted_text_output, attributes_output])
-
-            export_button = gr.Button("OK")
-            result_output = gr.Text(label="🚀 Result")
-
-            def export_attributes(mode, entry_type, quantity, attributes_list):
-                attributes = {item[0]: item[1] for item in attributes_list}
-                message = export_to_salesforce(mode, entry_type, quantity, attributes)
-                return message
-
-            export_button.click(export_attributes, inputs=[mode_input, entry_type_input, quantity_input, attributes_output], outputs=[result_output])
-
-        with gr.Tab("📊 Salesforce Data Export"):
-            selected_object_input = gr.Dropdown(label="Select Salesforce Object", choices=["Inventory_Management__c", "Un_Billable__c"], value="Inventory_Management__c")
-            status_output = gr.Text(label="Status")
-            data_table_output = gr.Dataframe(label="📦 Salesforce Data Table")
-            download_link_output = gr.File(label="Download Salesforce Data")
-            bar_graph_output = gr.Image(label="📉 Stock Distribution Bar Graph")
-            pull_data_button = gr.Button("Pull Data")
-
-            def pull_data(selected_object):
-                status, df, excel_path, img = pull_data_from_salesforce(selected_object)
-                return status, df, excel_path, img
-
-            pull_data_button.click(pull_data, inputs=[selected_object_input], outputs=[status_output, data_table_output, download_link_output, bar_graph_output])
-
-    demo.launch(share=True)
+    return gr.TabbedInterface([
+        gr.Interface(
+            fn=process_image,
+            inputs=[
+                gr.Image(type="numpy", label="📄 Upload Image"),
+                gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry"),
+                gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales"),
+                gr.Number(label="🔢 Quantity", value=1, interactive=True),
+            ],
+            outputs=[
+                gr.Text(label="📝 Extracted Image Data"),
+                gr.Dataframe(label="✏️ Edit Attributes (Key-Value Pairs)"),
+                gr.Text(label="🚀 Result")
+            ],
+            title="🏢 𝑽𝑬𝑵𝑲𝑨𝑻𝑨𝑹𝑨𝑴𝑨𝑵𝑨 𝑴𝑶𝑻𝑶𝑹𝑺",
+            description="📦 𝐈𝐍𝐕𝐄𝐍𝐓𝐎𝐑𝐘 𝐌𝐀𝐍𝐀𝐆𝐄𝐌𝐄𝐍𝐓"
+        ),
+        gr.Interface(
+            fn=export_to_salesforce,
+            inputs=[
+                gr.Dropdown(label="📌 Mode", choices=["Entry", "Exit"], value="Entry"),
+                gr.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales"),
+                gr.Number(label="🔢 Quantity", value=1, interactive=True),
+                gr.Dataframe(label="✏️ Edited Attributes (Key-Value Pairs)")
+            ],
+            outputs=[
+                gr.Text(label="🚀 Result")
+            ],
+            title="📤 Export to Salesforce",
+            description="Edit attributes and export to Salesforce."
+        )
+    ], ["📥 OCR Processing", "📤 Export to Salesforce"])
 
 if __name__ == "__main__":
-    app()
+    app().launch(share=True)