Update app.py

app.py

@@ -6,8 +6,8 @@ import requests
 import re
 from simple_salesforce import Salesforce
 import pandas as pd
-import plotly.graph_objects as go
-import kaleido  # Ensure kaleido is imported for saving the plot
+import matplotlib.pyplot as plt
+from io import BytesIO
 from fuzzywuzzy import process
 
 # Attribute mappings: readable names to Salesforce API names
@@ -74,6 +74,35 @@ ATTRIBUTE_MAPPING = {
     "coolingmethod": "coolingmethod__c"
 }
 
+# List of product names to match
+PRODUCT_NAMES = [
+    "Centrifugal mono block pump", "SINGLE PHASE MOTOR STARTER", "EasyPact EZC 100",
+    "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 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",
+    "Havells open well Submersible pump", "Bertolini pump CK3 90pp",
+    "WPA 772 Water Pump Assy", "bertolini TTL triplex high pressure plunger pumps",
+    "Generic plunger high pressure pump", "Apple Normal, Banana",
+    "Cast Iron KSb centrifugal pump", "5.5kw Water Pump",
+    "KSB reliable i line centrifuged pumps", "Apple Normal, Orange, Banana",
+    "Positive API 6745 hydraulic diaphragm pump", "1/2 inch Fuel Hose Pipe", "Kirloskar Water Pump",
+    "Rotodel motor pump", "PVC Electrical Insulation Materials",
+    "Electric kirloskar domestic water pump", "Electrical Insulation Materials",
+    "sellowell motor pump", "bhupathi submersible pump set",
+    "Flowshine Submersible pump set", "Index submersible pump",
+    "Wintoss Plastic Electric Switch Board", "Electric 18 watt ujagar cooler pump",
+    "Generator Service", "LG WM FHT1207ZWL, LG REF GL-S292RSCY",
+    "Water tank, Filters, Water Pump", "MS Control Submersible Panel",
+    "Centrifugal Monoblock Pumps", "Electric Motor with Pump BodyBlue and White",
+    "Various Repair and Maintenance Parts", "Earthmax Pump",
+    "Water Tank, Filters, Water Pump", "Centrifugal Water Pump for Agriculture",
+    "mono block pumps"
+]
+
 # Salesforce credentials
 SALESFORCE_USERNAME = "venkatramana@sandbox.com"
 SALESFORCE_PASSWORD = "Venkat12345@"
@@ -92,32 +121,33 @@ def extract_text(image):
 
 # Function to match product name using fuzzy matching
 def match_product_name(extracted_text):
-    PRODUCT_NAMES = [
-        "Centrifugal mono block pump", "SINGLE PHASE MOTOR STARTER", "EasyPact EZC 100",
-        "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", "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",
-        "Havells open well Submersible pump", "Bertolini pump CK3 90pp", "WPA 772 Water Pump Assy", "bertolini TTL triplex high pressure plunger pumps",
-        "Generic plunger high pressure pump", "Apple Normal, Banana", "Cast Iron KSb centrifugal pump", "5.5kw Water Pump",
-        "KSB reliable i line centrifuged pumps", "Apple Normal, Orange, Banana", "Positive API 6745 hydraulic diaphragm pump",
-        "1/2 inch Fuel Hose Pipe", "Kirloskar Water Pump", "Rotodel motor pump", "PVC Electrical Insulation Materials", "Electric kirloskar domestic water pump",
-        "Electrical Insulation Materials", "sellowell motor pump", "bhupathi submersible pump set", "Flowshine Submersible pump set",
-        "Index submersible pump", "Wintoss Plastic Electric Switch Board", "Electric 18 watt ujagar cooler pump",
-        "Generator Service", "LG WM FHT1207ZWL, LG REF GL-S292RSCY", "Water tank, Filters, Water Pump", "MS Control Submersible Panel",
-        "Centrifugal Monoblock Pumps", "Electric Motor with Pump BodyBlue and White", "Various Repair and Maintenance Parts", "Earthmax Pump",
-        "Water Tank, Filters, Water Pump", "Centrifugal Water Pump for Agriculture", "mono block pumps"
-    ]
     best_match = None
     best_score = 0
+
     for line in extracted_text.split("\n"):
         match, score = process.extractOne(line, PRODUCT_NAMES)
         if score > best_score:
             best_match = match
             best_score = score
+
     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):
+    attributes = {}
+
+    for readable_attr, sf_attr in ATTRIBUTE_MAPPING.items():
+        pattern = rf"{re.escape(readable_attr)}[:\-]?\s*(.+)"
+        match = re.search(pattern, extracted_text, re.IGNORECASE)
+        if match:
+            attributes[readable_attr] = match.group(1).strip()
+
+    return attributes
+
+# Function to filter attributes for valid Salesforce fields
+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
 def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
     try:
@@ -130,8 +160,8 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
         # Mapping mode and entry_type to Salesforce object and field
         object_name = None
         field_name = None
-        product_field_name = "Product_Name__c"
-        model_field_name = "Modal_Name__c"
+        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":
@@ -143,9 +173,13 @@ 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:
@@ -158,72 +192,116 @@ def interact_with_salesforce(mode, entry_type, quantity, extracted_text):
 
         # Extract product name or model number
         product_name = match_product_name(extracted_text)
+        attributes = extract_attributes(extracted_text)
+
         if not product_name:
             return "Product name could not be matched from the extracted text."
 
-        # Continue with further processing (creating or updating Salesforce data)
-        return "Processed successfully."
+        attributes["Product name"] = product_name
+
+        if mode == "Exit":
+            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"]
+                current_quantity = response["records"][0].get(field_name, 0) or 0
+                updated_quantity = max(0, current_quantity + quantity)
+                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}."
+            else:
+                return f"No matching record found for product '{product_name}' in {object_name}."
+        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}."
 
     except Exception as e:
-        return f"❌ Error interacting with Salesforce: {str(e)}"
+        return f"Error interacting with Salesforce: {str(e)}"
 
-# Function to retrieve Salesforce data
-def pull_data_from_salesforce():
+# Function to pull data from Salesforce MotorDataAPI
+def pull_data_from_motor_api():
     try:
         sf = Salesforce(
             username=SALESFORCE_USERNAME,
             password=SALESFORCE_PASSWORD,
             security_token=SALESFORCE_SECURITY_TOKEN
         )
+        motor_data = sf.apexecute("MotorDataAPI/", method="GET")
+        return motor_data  # API returns the list of records
+    except Exception as e:
+        return f"Error pulling data from MotorDataAPI: {str(e)}"
 
-        query = "SELECT Product_Name__c, Model_Name__c, Current_Stocks__c FROM Inventory_Management__c LIMIT 100"
-        response = sf.query_all(query)
+# Function to format Salesforce data into a DataFrame with readable headers
+def format_salesforce_data():
+    try:
+        data = pull_data_from_motor_api()
+        if isinstance(data, list):
+            df = pd.DataFrame(data)
+            df = df.rename(columns={
+                "Product_Name__c": "Product Name",
+                "Modal_Name__c": "Model Name",
+                "Current_Stocks__c": "Current Stocks"
+            })
+            # Remove unnecessary columns
+            df = df[["Product Name", "Model Name", "Current Stocks"]]
+            return df
+        else:
+            return None
+    except Exception as e:
+        return None
 
-        records = response.get("records", [])
-        if not records:
-            return "No data found in Salesforce.", None
+# 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)
+        img = Image.open(buffer)
+        return img
+    except Exception as e:
+        return None
 
-        df = pd.DataFrame(records)
-        df = df.drop(columns=['attributes'], errors='ignore')
-        df.rename(columns={
-            "Product_Name__c": "Product Name",
-            "Model_Name__c": "Model Name",
-            "Current_Stocks__c": "Current Stocks"
-        }, inplace=True)
+# Unified function to handle image processing and Salesforce interaction
+def process_image(image, mode, entry_type, quantity, state):
+    extracted_text = extract_text(image)
+    if not extracted_text:
+        return "No text detected in the image.", None
 
-        return "Data successfully retrieved.", df
-    except Exception as e:
-        return f"Error fetching data: {str(e)}", None
+    product_name = match_product_name(extracted_text)
+    attributes = extract_attributes(extracted_text)
+    if product_name:
+        attributes["Product name"] = product_name
 
-# Function to generate the graph based on Salesforce data
-def generate_graph(df):
-    try:
-        fig = go.Figure()
-        fig.add_trace(go.Bar(
-            x=df['Product Name'],
-            y=df['Current Stocks'],
-            marker=dict(color='blue'),
-            hoverinfo='x+y',
-            hovertemplate='<b>Product Name:</b> %{x}<br><b>Current Stocks:</b> %{y}<extra></extra>',
-        ))
-
-        fig.update_layout(
-            title="Current Stocks of Products",
-            xaxis=dict(title="Product Name", tickangle=-45),
-            yaxis=dict(title="Current Stocks"),
-            showlegend=False
-        )
+    # Interact with Salesforce
+    message = interact_with_salesforce(mode, entry_type, quantity, extracted_text)
 
-        return fig
-    except Exception as e:
-        return f"Error generating graph: {str(e)}"
+    # Fetch updated data and graph
+    df = format_salesforce_data()
+    if df is not None:
+        table_component = df.to_html(index=False)
+        bar_graph_image = generate_bar_graph(df)
+        if bar_graph_image:
+            state["graph"] = bar_graph_image
+            state["table"] = table_component
 
-# Main Gradio interface function
+    numbered_output = "\n".join([f"{key}: {value}" for key, value in attributes.items()])
+    return f"Extracted Text:\n{extracted_text}\n\nAttributes and Values:\n{numbered_output}", message
+
+# Gradio Interface
 def app():
-    data_status, data_table = pull_data_from_salesforce()
-    
     with gr.Blocks() as demo:
-        with gr.Tab("📦 OCR Processing"):
+        state = gr.State({"table": "", "graph": None})  # State for storing table and graph
+        
+        with gr.Tab("📦 𝑷𝑹𝑶𝑪𝑬𝑺𝑺 𝑰𝑴𝑨𝑮𝑬"):
             gr.Interface(
                 fn=process_image,
                 inputs=[
@@ -233,20 +311,20 @@ def app():
                     gr.Number(label="🔢 Quantity", value=1, interactive=True),
                 ],
                 outputs=[
-                    gr.Text(label="📝 Extracted Text"),
+                    gr.Text(label="📝 Extracted Image Data"),
                     gr.Text(label="🚀 Result")
                 ],
                 title="🏢 VENKATARAMANA MOTORS",
                 description="📦 Inventory Management System"
             )
 
-        with gr.Tab("📊 Salesforce Data Export"):
+        with gr.Tab("📊 SALESFORCE DATA EXPORT"):
             gr.Markdown("## 📊 Salesforce Data Export")
-            status_text = gr.Textbox(value=data_status, interactive=False)
-            table_output = gr.DataFrame(value=data_table if data_table is not None else "No Data Available", interactive=False)
-            graph_output = gr.Plot(value=generate_graph(data_table) if data_table is not None else None, interactive=False)
+            table_output = gr.HTML(value=state["table"])
+            graph_output = gr.Image(value=state["graph"], type="pil")
+
+    return demo
 
-    demo.launch(share=True)
 
 if __name__ == "__main__":
-    app()
+    app().launch(share=True)