Update app.py

app.py

@@ -1,18 +1,5 @@
 import os
 from paddleocr import PaddleOCR
-import gradio as gr
-from simple_salesforce import Salesforce
-import pandas as pd
-import plotly.express as px
-import re
-from fuzzywuzzy import process
-
-# Load Salesforce Credentials Securely
-SALESFORCE_USERNAME = os.getenv("SALESFORCE_USERNAME")
-SALESFORCE_PASSWORD = os.getenv("SALESFORCE_PASSWORD")
-SALESFORCE_SECURITY_TOKEN = os.getenv("SALESFORCE_SECURITY_TOKEN")
-import os
-from paddleocr import PaddleOCR
 from PIL import Image
 import gradio as gr
 import requests
@@ -116,6 +103,11 @@ PRODUCT_NAMES = [
     "mono block pumps"
 ]
 
+#📌 Salesforce credentials
+SALESFORCE_USERNAME = "venkatramana@sandbox.com"
+SALESFORCE_PASSWORD = "Venkat12345@"
+SALESFORCE_SECURITY_TOKEN = "GhcJJmjBEefdnukJoz4CAQlR"
+
 #🧠 Initialize PaddleOCR
 ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
@@ -294,91 +286,36 @@ def process_image(image, mode, entry_type, quantity):
     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
 
-import os
-from paddleocr import PaddleOCR
-import gradio as gr
-from simple_salesforce import Salesforce
-import pandas as pd
-import plotly.express as px
-import re
-from fuzzywuzzy import process
-
-# Initialize PaddleOCR
-ocr = PaddleOCR(use_angle_cls=True, lang='en')
-
-# Salesforce Credentials
-SALESFORCE_USERNAME = os.getenv("SALESFORCE_USERNAME")
-SALESFORCE_PASSWORD = os.getenv("SALESFORCE_PASSWORD")
-SALESFORCE_SECURITY_TOKEN = os.getenv("SALESFORCE_SECURITY_TOKEN")
-
-# Fetch Salesforce Data & Generate Graph
-def fetch_salesforce_data():
-    try:
-        sf = Salesforce(username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN)
-        query = "SELECT Product_Name__c, Current_Stocks__c FROM Inventory_Management__c"
-        response = sf.query_all(query)
-        
-        if not response['records']:
-            return "No data found in Salesforce.", None
-        
-        df = pd.DataFrame(response["records"]).drop(columns=['attributes'], errors='ignore')
-        df.rename(columns={"Product_Name__c": "Product Name", "Current_Stocks__c": "Stock Quantity"}, inplace=True)
-        
-        fig = px.bar(
-            df, 
-            x="Product Name", 
-            y="Stock Quantity", 
-            title="Stock Distribution",
-            labels={"Product Name": "Products", "Stock Quantity": "Quantity"},
-            hover_data=["Product Name", "Stock Quantity"],
-            color="Stock Quantity",
-            color_continuous_scale='Viridis'
-        )
-
-        fig.update_traces(
-            texttemplate='%{x}: %{y}', 
-            textposition='outside', 
-            hovertemplate='%{x}: %{y} units'
-        )
-
-        fig.update_layout(
-            hovermode='closest',
-            xaxis_title="Product",
-            yaxis_title="Stock Quantity",
-            template="plotly_dark",
-            showlegend=False
-        )
-
-        return df.to_html(index=False), fig.to_image(format="png")
-    
-    except Exception as e:
-        return f"Error fetching Salesforce data: {str(e)}", None
-
-# Image Processing Function
-def process_image(image, mode, entry_type, quantity):
-    extracted_text = "Sample Extracted Text"  # Placeholder for OCR result
-    return f"Extracted Text:\n{extracted_text}\n\nProcessed successfully."
-
 # Gradio Interface
 def app():
+    df = format_salesforce_data()
+    table_component = None
+    bar_graph_component = None
+
+    if df is not None:
+        table_component = df.to_html(index=False)
+        bar_graph_image = generate_bar_graph(df)
+        if bar_graph_image:
+            bar_graph_component = bar_graph_image
+
     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),
+                gr.Image(type="numpy", label="📄ᴜᴘʟᴏᴀᴅ ɪᴍᴀɢᴇ"),
+                gr.Dropdown(label="📌Mode", choices=["ᴇɴᴛʀʏ", "ᴇxɪᴛ"], 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="📝 Image Data Viewer"),
-                gr.Text(label="📝 Result")
+                gr.Text(label="ɪᴍᴀɢᴇ ᴅᴀᴛᴀ ᴠɪᴇᴡᴇʀ"),
+                gr.Text(label="📝ＲＥＳＵＬＴ")
             ],
             title="𝑽𝑬𝑵𝑲𝑨𝑻𝑨𝑹𝑨𝑴𝑨𝑵𝑨 𝑴𝑶𝑻𝑶𝑹𝑺",
-            description="📦 𝐈𝐍𝐕𝐄𝐍𝐓𝐎𝐑𝐘 𝐌𝐀𝐍𝐀𝐆𝐄𝐌𝐄𝐍���"
+            description="📦𝐈𝐍𝐕𝐄𝐍𝐓𝐎𝐑𝐘 𝐌𝐀𝐍𝐀𝐆𝐄𝐌𝐄𝐍𝐓"
         ),
         gr.Interface(
-            fn=fetch_salesforce_data,
+            fn=lambda: (table_component, bar_graph_component),
             inputs=[],
             outputs=[
                 gr.HTML(label="Salesforce Data Table"),
@@ -388,8 +325,7 @@ def app():
             description="View structured Salesforce data as a table and bar graph."
         )
     ], ["Processing", "Salesforce Data"])
-
-# 🎨 Bright and Gradient UI with Updated Coloring
+    # 🎨 Bright and Gradient UI with Updated Coloring
 css = """
     body {
         background: linear-gradient(135deg, #282c34, #4b79a1);
@@ -417,6 +353,25 @@ css = """
         animation: glow 2s infinite alternate;
     }
 
+    @keyframes glow {
+        from {
+            text-shadow: 5px 5px 30px rgba(255, 87, 134, 0.6);
+        }
+        to {
+            text-shadow: 6px 6px 40px rgba(255, 54, 90, 1);
+        }
+    }
+
+    .gradio-box {
+        border-radius: 15px;
+        padding: 25px;
+        background: linear-gradient(135deg, #6a11cb, #2575fc);
+        box-shadow: 0px 6px 25px rgba(30, 144, 255, 0.6);
+        border: 2px solid #6a5acd;
+        color: white;
+        font-size: 18px;
+    }
+
     .gradio-button {
         border-radius: 12px;
         padding: 18px 36px;
@@ -433,7 +388,45 @@ css = """
         box-shadow: 0px 10px 35px rgba(0, 191, 255, 0.9);
         transform: scale(1.1);
     }
+
+    .gradio-input {
+        border-radius: 10px;
+        padding: 16px;
+        font-size: 18px;
+        background: rgba(255, 255, 255, 0.3);
+        border: 2px solid rgba(0, 123, 255, 0.5);
+        color: #fff;
+        transition: 0.3s;
+    }
+
+    .gradio-input:focus {
+        border: 2px solid #1e90ff;
+        outline: none;
+        box-shadow: 0px 5px 20px rgba(30, 144, 255, 0.6);
+    }
+
+    .gradio-output {
+        background: linear-gradient(135deg, #f39c12, #e74c3c);
+        padding: 22px;
+        border-radius: 15px;
+        color: white;
+        font-size: 20px;
+        text-align: center;
+        border: 2px solid #f39c12;
+    }
+
+    .gradio-file {
+        background: linear-gradient(135deg, #8e44ad, #3498db);
+        color: white;
+        padding: 16px;
+        border-radius: 15px;
+        text-align: center;
+        font-size: 18px;
+    }
 """
 
+
+
+
 if __name__ == "__main__":
-    app().launch(share=True)
+    app().launch(share=True)