Update app.py

app.py

@@ -5,12 +5,13 @@ import gradio as gr
 import re
 from simple_salesforce import Salesforce
 import pandas as pd
+
 # Attribute mappings: readable names to Salesforce API names
 ATTRIBUTE_MAPPING = {
     "Name": "Name__c",
     "Age": "Age__c",
     "Gender": "Gender__c",
-    "Phone Number": "Phone__c"
+    "Phone Number": "Phone_Number__c"
 }
 
 # Salesforce credentials
@@ -38,7 +39,7 @@ def extract_attributes(extracted_text):
         "Name": r"Name[:\-]?\s*([A-Za-z\s]+)",
         "Age": r"Age[:\-]?\s*(\d{1,3})",
         "Gender": r"Gender[:\-]?\s*(Male|Female|Other)",
-        "Phone Number": r"Phone[:\-]?\s*(\+?\d{10,12})"
+        "Phone Number": r"(?:Phone|Mobile)[:\-]?\s*(\+?\d{10,12})"
     }
 
     for readable_attr, pattern in patterns.items():
@@ -57,17 +58,21 @@ def interact_with_salesforce(attributes):
     try:
         sf = Salesforce(
             username=SALESFORCE_USERNAME,
-            password=SALESFORCE_PASSWORD,
+            password=S=SALESFORCE_PASSWORD,
             security_token=SALESFORCE_SECURITY_TOKEN
         )
 
-        object_name = "Patient_Registration__c"  # Using custom Patient Registration object
+        object_name = "Patient_Registration__c"
         sf_object = sf.__getattr__(object_name)
         schema = sf_object.describe()
         valid_fields = {field["name"] for field in schema["fields"]}
 
         filtered_attributes = filter_valid_attributes(attributes, valid_fields)
 
+        # Ensure Age__c is a number
+        if "Age__c" in filtered_attributes:
+            filtered_attributes["Age__c"] = int(filtered_attributes["Age__c"])
+
         # Create a new record in Salesforce
         result = sf_object.create(filtered_attributes)
         return f"✅ Successfully created Patient Registration record with ID: {result['id']}."
@@ -105,55 +110,6 @@ def export_to_salesforce(edited_df):
     except Exception as e:
         return f"❌ Error exporting to Salesforce: {str(e)}"
 
-# Function to pull structured data from Salesforce and display as a table
-def pull_data_from_salesforce():
-    try:
-        sf = Salesforce(
-            username=SALESFORCE_USERNAME,
-            password=SALESFORCE_PASSWORD,
-            security_token=SALESFORCE_SECURITY_TOKEN
-        )
-
-        query = "SELECT Name__c, Age__c, Gender__c, Phone__c FROM Patient_Registration__c WHERE Age__c != NULL LIMIT 100"
-        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={
-            "Name__c": "Name",
-            "Age__c": "Age",
-            "Gender__c": "Gender",
-            "Phone__c": "Phone Number"
-        }, inplace=True)
-
-        excel_path = "salesforce_patient_registration.xlsx"
-        df.to_excel(excel_path, index=False)
-
-        # Generate a bar graph for age distribution
-        fig, ax = plt.subplots(figsize=(12, 8))
-        df['Age'] = pd.to_numeric(df['Age'], errors='coerce')
-        df.groupby('Age').size().plot(kind='bar', ax=ax)
-        ax.set_title("Age Distribution of Patient Registrations")
-        ax.set_xlabel("Age")
-        ax.set_ylabel("Number of Patients")
-        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 df, excel_path, img
-
-    except Exception as e:
-        return f"Error fetching data: {str(e)}", None, None, None
-
 # Gradio Interface
 def app():
     with gr.Blocks() as demo:
@@ -166,12 +122,6 @@ def app():
             ok_button = gr.Button("OK")
             result_output = gr.Text(label="🚀 Result")
 
-        with gr.Tab("📊 Salesforce Data"):
-            pull_button = gr.Button("Pull Data from Salesforce")
-            salesforce_data_output = gr.Dataframe(label="📊 Salesforce Data")
-            excel_download_output = gr.File(label="📥 Download Excel")
-            graph_output = gr.Image(label="📈 Age Distribution Graph")
-
         # Define button actions
         extract_button.click(
             fn=process_image,
@@ -183,11 +133,6 @@ def app():
             inputs=[editable_df_output],
             outputs=[result_output]
         )
-        pull_button.click(
-            fn=pull_data_from_salesforce,
-            inputs=[],
-            outputs=[salesforce_data_output, excel_download_output, graph_output]
-        )
 
     return demo