Update app.py

app.py

@@ -4,7 +4,8 @@ import os
 from dotenv import load_dotenv
 from simple_salesforce import Salesforce
 from datetime import datetime
-import random  # For mock predictions
+import random
+import shutil
 
 # Load environment variables
 load_dotenv()
@@ -22,55 +23,62 @@ try:
         username=SF_USERNAME,
         password=SF_PASSWORD,
         security_token=SF_SECURITY_TOKEN,
-        domain='login'  # Use 'test' for sandbox
+        domain='login'
     )
 except Exception as e:
     print(f"Salesforce connection failed: {str(e)}")
     raise
 
-# ✅ Valid milestone values from Salesforce picklist
+# Valid milestones
 VALID_MILESTONES = ["Foundation", "Walls Erected", "Planning", "Completed"]
 
-# Mock AI model for milestone detection (since we can't train a real model here)
+# Mock AI prediction
 def mock_ai_model(image):
-    # Preprocessing: Resize, normalize (simulated)
     img = image.convert("RGB")
     max_size = 1024
     img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
-
-    # Simulate milestone detection by picking a random valid value
     milestone = random.choice(VALID_MILESTONES)
-    completion_percent = random.choice([10, 30, 50, 80, 100])  # Just for variety
+    completion_percent = random.choice([10, 30, 50, 80, 100])
     confidence_score = round(random.uniform(0.85, 0.95), 2)
-
     return milestone, completion_percent, confidence_score
 
-# Function for Gradio UI to process the image
-def process_image(image):
+# Gradio function
+def process_image(image, project_name):
     try:
         if image is None:
             return "Error: Please upload an image to proceed.", "Pending", "", "", 0
 
         img = Image.open(image)
-
         image_size_mb = os.path.getsize(image) / (1024 * 1024)
         if image_size_mb > 20:
             return "Error: Image size exceeds 20MB.", "Failure", "", "", 0
         if not str(image).lower().endswith(('.jpg', '.jpeg', '.png')):
             return "Error: Only JPG/PNG images are supported.", "Failure", "", "", 0
 
-        # Run mock AI model
-        milestone, percent_complete, confidence_score = mock_ai_model(img)
-
+        # Save image to public folder for URL generation
+        upload_dir = "public_uploads"
+        os.makedirs(upload_dir, exist_ok=True)
         unique_id = datetime.now().strftime("%Y%m%d%H%M%S")
+        image_filename = f"{unique_id}_{os.path.basename(image)}"
+        saved_image_path = os.path.join(upload_dir, image_filename)
+        shutil.copy(image, saved_image_path)
 
+        # Create public URL assuming you're serving /public_uploads/ via static web server (e.g., on localhost or external host)
+        public_url_base = os.getenv("PUBLIC_URL_BASE", "http://localhost:7860/public_uploads")
+        image_url = f"{public_url_base}/{image_filename}"
+
+        # Predict
+        milestone, percent_complete, confidence_score = mock_ai_model(img)
+
+        # Construct Salesforce record
         record = {
-            "Name__c": unique_id,
+            "Name__c": project_name,
             "Current_Milestone__c": milestone,
             "Completion_Percentage__c": percent_complete,
             "Last_Updated_On__c": datetime.now().isoformat(),
             "Upload_Status__c": "Success",
-            "Comments__c": f"AI Prediction: {milestone} with {confidence_score*100}% confidence"
+            "Comments__c": f"AI Prediction: {milestone} with {confidence_score*100}% confidence",
+            "Last_Updated_Image__c": image_url
         }
 
         try:
@@ -89,10 +97,13 @@ def process_image(image):
     except Exception as e:
         return f"Error: {str(e)}", "Failure", "", "", 0
 
-# Gradio interface for testing
+# Gradio UI
 with gr.Blocks(css=".gradio-container {background-color: #f0f4f8; font-family: Arial;} .title {color: #2c3e50; font-size: 24px; text-align: center;}") as demo:
     gr.Markdown("<h1 class='title'>Construction Milestone Detector</h1>")
-    image_input = gr.Image(type="filepath", label="Upload Construction Site Photo (JPG/PNG, ≤ 20MB)")
+    with gr.Row():
+        image_input = gr.Image(type="filepath", label="Upload Construction Site Photo (JPG/PNG, ≤ 20MB)")
+        project_name_input = gr.Textbox(label="Project Name (Required)", placeholder="e.g. Project_12345")
+
     submit_button = gr.Button("Process Image")
     output_text = gr.Textbox(label="Result")
     upload_status = gr.Textbox(label="Upload Status")
@@ -102,7 +113,7 @@ with gr.Blocks(css=".gradio-container {background-color: #f0f4f8; font-family: A
 
     submit_button.click(
         fn=process_image,
-        inputs=[image_input],
+        inputs=[image_input, project_name_input],
         outputs=[output_text, upload_status, milestone, confidence, progress]
     )