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README.md

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----
-title: Inventory
-emoji: ⚡
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 5.35.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
+# 🚀 Salesforce Inventory OCR App
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This app lets you upload invoice/product images, extract product attributes using OCR, and update Salesforce inventory. It also allows downloading and visualizing stock data.
+
+## Features
+- OCR via PaddleOCR
+- Editable product table
+- Push Entry/Exit data to Salesforce
+- Fetch inventory and export to Excel
+- Visual stock bar chart
+
+## To Run
+1. Install dependencies: `pip install -r requirements.txt`
+2. Set your Salesforce credentials via environment variables:
+   - `SF_USERNAME`
+   - `SF_PASSWORD`
+   - `SF_TOKEN`
+3. Run: `python app.py`

main.py

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+import gradio as gr
+from ocr_utils import process_image
+from salesforce_utils import push_data_to_salesforce, fetch_inventory_data
+from viz_utils import generate_stock_chart
+import pandas as pd
+
+uploaded_data = []
+
+def handle_upload(image, mode, entry_type):
+    global uploaded_data
+    data = process_image(image)
+    df = pd.DataFrame(data)
+    uploaded_data = df
+    return df
+
+def update_salesforce(dataframe, mode, entry_type):
+    df = pd.DataFrame(dataframe)
+    status = push_data_to_salesforce(df, mode, entry_type)
+    return status
+
+def download_inventory():
+    inventory = fetch_inventory_data()
+    return inventory, inventory.to_csv(index=False)
+
+def show_chart():
+    df = fetch_inventory_data()
+    chart_path = generate_stock_chart(df)
+    return chart_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🚀 Salesforce Inventory OCR App")
+    with gr.Row():
+        image = gr.Image(type="filepath", label="Upload Invoice or Label")
+        mode = gr.Dropdown(["Entry", "Exit"], label="Select Mode")
+        entry_type = gr.Dropdown(["Sales", "Non-Sales"], label="Entry Type")
+    upload_btn = gr.Button("Extract & Show Data")
+    table = gr.Dataframe(label="Editable Product Table", interactive=True)
+    upload_btn.click(handle_upload, inputs=[image, mode, entry_type], outputs=table)
+
+    update_btn = gr.Button("Update Salesforce")
+    update_status = gr.Textbox(label="Update Status")
+    update_btn.click(update_salesforce, inputs=[table, mode, entry_type], outputs=update_status)
+
+    download_btn = gr.Button("Download Inventory from Salesforce")
+    inventory_table = gr.Dataframe()
+    csv_file = gr.File()
+    download_btn.click(download_inventory, inputs=[], outputs=[inventory_table, csv_file])
+
+    chart_btn = gr.Button("Visualize Stock Levels")
+    chart_output = gr.Image()
+    chart_btn.click(show_chart, inputs=[], outputs=chart_output)
+
+demo.launch()

ocr_utils.py

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+from paddleocr import PaddleOCR
+import cv2
+
+ocr_model = PaddleOCR(use_angle_cls=True, lang='en')
+
+def process_image(image_path):
+    result = ocr_model.ocr(image_path, cls=True)
+    extracted_data = []
+    for line in result[0]:
+        text = line[1][0]
+        # Simple parser, in real case you’ll parse with regex or templates
+        extracted_data.append({
+            "Product Name": text,
+            "Model": "",
+            "HP": "",
+            "Stage": "",
+            "Price": ""
+        })
+    return extracted_data

requirements.txt

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+paddleocr
+gradio
+simple-salesforce
+pandas
+matplotlib
+fuzzywuzzy
+kaleido

salesforce_utils.py

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+from simple_salesforce import Salesforce
+import pandas as pd
+import os
+
+sf = Salesforce(username=os.getenv("SF_USERNAME"),
+                password=os.getenv("SF_PASSWORD"),
+                security_token=os.getenv("SF_TOKEN"))
+
+def push_data_to_salesforce(df, mode, entry_type):
+    object_name = "Inventory_Entry__c" if mode == "Entry" else "Inventory_Exit__c"
+    for _, row in df.iterrows():
+        try:
+            sf.__getattr__(object_name).create({
+                "Product_Name__c": row["Product Name"],
+                "Product_Model__c": row["Model"],
+                "Horsepower__c": row["HP"],
+                "Stage__c": row["Stage"],
+                "Entry_Type__c": entry_type,
+                "Price__c": row["Price"]
+            })
+        except Exception as e:
+            return f"Error pushing to Salesforce: {e}"
+    return "✅ Data successfully pushed to Salesforce."
+
+def fetch_inventory_data():
+    query = "SELECT Product_Name__c, Stock__c FROM Inventory__c"
+    result = sf.query_all(query)
+    records = result["records"]
+    return pd.DataFrame([{"Product": r["Product_Name__c"], "Stock": r["Stock__c"]} for r in records])

viz_utils.py

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+import matplotlib.pyplot as plt
+
+def generate_stock_chart(df):
+    df = df.sort_values(by="Stock")
+    plt.figure(figsize=(10, 6))
+    plt.barh(df["Product"], df["Stock"], color='skyblue')
+    plt.xlabel("Stock Quantity")
+    plt.title("Inventory Stock Distribution")
+    plt.tight_layout()
+    path = "/mnt/data/stock_chart.png"
+    plt.savefig(path)
+    return path