Update app.py

app.py

@@ -9,7 +9,6 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from io import BytesIO
 from fuzzywuzzy import process
-import plotly.graph_objects as go
 import kaleido  # Ensure kaleido is imported
 
 # Attribute mappings: readable names to Salesforce API names
@@ -82,7 +81,7 @@ PRODUCT_NAMES = [
     "Openwell Submersible Pumpset", "Electric Motor", "Self Priming Pump",
     "Control panel for single phase submersible pumps", "MOTOR", "Submersible pump set",
     "Fusion submersible pump set", "DCT", "Shock proof water proof", "CG COMMERCIAL MOTORS", "Fusion",
-    "control panel for single phase submersible pumps",
+    "control panel for single phase submerisible pumps",
     "single phase digital starter dry run and timer panel", "5HP AV1 XL Kirloskar Pump",
     "Phase stainless steel submersible pump", "Submersible pump", "WB15X",
     "Vtype self priming pump", "SP SHINE DISC", "havells submersible pump",
@@ -150,7 +149,7 @@ def extract_attributes(extracted_text):
 def filter_valid_attributes(attributes, valid_fields):
     return {ATTRIBUTE_MAPPING[key]: value for key, value in attributes.items() if ATTRIBUTE_MAPPING[key] in valid_fields}
 
-# Function to interact with Salesforce based on mode and type
+#📊  Function to interact with Salesforce based on mode and type
 def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
     try:
         sf = Salesforce(
@@ -159,11 +158,11 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
             security_token=SALESFORCE_SECURITY_TOKEN
         )
 
-        # Mapping mode and entry_type to Salesforce object and field
+        # Determine Salesforce Object and Fields
         object_name = None
+        product_field_name = "Product_Name__c"
+        model_field_name = "Modal_Name__c"
         field_name = None
-        product_field_name = "Product_Name__c"  # Correct field for product name in the object
-        model_field_name = "Modal_Name__c"  # Correct field for model name in the object
 
         if mode == "Entry":
             if entry_type == "Sales":
@@ -175,79 +174,52 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
         elif mode == "Exit":
             if entry_type == "Sales":
                 object_name = "Inventory_Management__c"
-                product_field_name = "Product_Name__c"
-                model_field_name = "Modal_Name__c"
                 field_name = "Quantity_Sold__c"
             elif entry_type == "Non-Sales":
                 object_name = "Un_Billable__c"
-                product_field_name = "Product_Name__c"
-                model_field_name = "Model_Name__c"
                 field_name = "Sold_Out__c"
 
         if not object_name or not field_name:
             return "Invalid mode or entry type."
 
-        # Get valid fields for the specified Salesforce object
-        sf_object = sf.__getattr__(object_name)
-        schema = sf_object.describe()
-        valid_fields = {field["name"] for field in schema["fields"]}
-
-        # Extract product name or model number
+        # Extract product name and attributes from the OCR result
         product_name = match_product_name(extracted_text)
         attributes = extract_attributes(extracted_text)
+        model_name = attributes.get("Model Name", "").strip()
 
-        if not product_name:
-            return "Product name could not be matched from the extracted text."
+        if not product_name and not model_name:
+            return "Product name or Model name could not be matched from the extracted text."
 
-        attributes["Product name"] = product_name
+        # Build strict query to match either Product Name or Model Name
+        query_conditions = []
+        if product_name:
+            query_conditions.append(f"{product_field_name} = '{product_name}'")
+        if model_name:
+            query_conditions.append(f"{model_field_name} = '{model_name}'")
+
+        query_condition_string = " OR ".join(query_conditions)
+
+        query = f"SELECT Id, {field_name} FROM {object_name} WHERE {query_condition_string} LIMIT 1"
+        response = sf.query(query)
 
-        # If in "Exit" mode, we check if the product name or model name is already in Salesforce
-        if mode == "Exit":
-            # Build query based on product name or model name
-            query = f"SELECT Id, {field_name} FROM {object_name} WHERE {product_field_name} = '{product_name}' OR {model_field_name} = '{attributes.get('Model Name', '')}' LIMIT 1"
-            response = sf.query(query)
-
-            if response["records"]:
-                record_id = response["records"][0]["Id"]
-                updated_quantity = quantity  # Overwrite the quantity, don't add
-                sf_object.update(record_id, {field_name: updated_quantity})
-                return f"Updated record for product '{product_name}' in {object_name}. New {field_name}: {updated_quantity}."
+        if response["records"]:
+            record_id = response["records"][0]["Id"]
+            existing_quantity = response["records"][0].get(field_name, 0)
+
+            # Ensure quantity is subtracted correctly in Exit Mode
+            if mode == "Exit":
+                updated_quantity = max(0, existing_quantity - quantity)
             else:
-                return f"No matching record found for product '{product_name}' in {object_name}."
+                updated_quantity = existing_quantity + quantity
+
+            sf.__getattr__(object_name).update(record_id, {field_name: updated_quantity})
+            return f"✅ Successfully updated product '{product_name or model_name}' in {object_name}. New {field_name}: {updated_quantity}."
         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}."
+            return f"❌ No matching record found for '{product_name or model_name}' in {object_name}."
 
     except Exception as e:
         return f"❌ Error interacting with Salesforce: {str(e)}"
 
-# Function to generate a simple bar graph from the DataFrame
-def generate_bar_graph(df):
-    fig = go.Figure()
-
-    # Create a simple bar graph from the data
-    fig.add_trace(go.Bar(
-        x=df['Product Name'],  # X-axis: Product Name
-        y=df['Current Stocks'],  # Y-axis: Current Stocks
-        marker=dict(color='skyblue'),  # Basic color for the bars
-        text=df['Current Stocks'],  # Show current stock as text on the bars
-        textposition='outside',  # Position text outside the bars
-    ))
-
-    # Update layout for cleaner styling
-    fig.update_layout(
-        title="Current Stocks of Products",  # Title of the chart
-        xaxis=dict(title="Product Name", tickangle=-45),  # X-axis label
-        yaxis=dict(title="Stock Quantity"),  # Y-axis label
-        showlegend=False,  # Hide legend
-        plot_bgcolor='white',  # White background for the plot
-        margin=dict(l=50, r=50, t=50, b=150),  # Adjust margins for better visibility
-    )
-
-    return fig
-
 # Function to pull structured data from Salesforce and display as a table
 def pull_data_from_salesforce():
     try:
@@ -277,10 +249,20 @@ def pull_data_from_salesforce():
         excel_path = "salesforce_data.xlsx"
         df.to_excel(excel_path, index=False)
        
-        # Call the function to generate the bar graph
-        fig = generate_bar_graph(df)
+        # 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, fig
+        return "Data successfully retrieved.", df, excel_path, img
     except Exception as e:
         return f"Error fetching data: {str(e)}", None, None, None
 
@@ -303,16 +285,16 @@ def process_image(image, mode, entry_type, quantity):
 
 # Gradio Interface
 def app():
-    return gr.TabbedInterface([  
+    return gr.TabbedInterface([
         gr.Interface(
             fn=process_image,
-            inputs=[  
+            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.Radio(label="📦 Entry Type", choices=["Sales", "Non-Sales"], value="Sales"),
                 gr.Number(label="🔢 Quantity", value=1, interactive=True),
             ],
-            outputs=[  
+            outputs=[
                 gr.Text(label="📝 Extracted Image Data"),
                 gr.Text(label="🚀 Result")
             ],
@@ -322,11 +304,11 @@ def app():
         gr.Interface(
             fn=pull_data_from_salesforce,
             inputs=[],
-            outputs=[  
+            outputs=[
                 gr.Text(label="Status"),
                 gr.Dataframe(label="📦 Salesforce Data Table"),
                 gr.File(label="Download Salesforce Data"),
-                gr.Plot(label="📉 Stock Distribution Bar Graph")
+                gr.Image(label="📉 Stock Distribution Bar Graph")
             ],
             title="📊 Salesforce Data Export",
             description="View, visualize (zoom-in/out), and download Salesforce data (Product Name, Model Name, Current Stocks)."
@@ -334,4 +316,4 @@ def app():
     ], ["📥 OCR Processing", "📊 Salesforce Data Export"])
 
 if __name__ == "__main__":
-    app().launch(share=True)
+    app().launch(share=True)