Update app.py

app.py

@@ -1,67 +1,9 @@
-import gradio as gr
-from PIL import Image
-import os
-from dotenv import load_dotenv
-from simple_salesforce import Salesforce
 from datetime import datetime
-import hashlib
-import shutil
-import base64
 import pytz
 
-# Load environment variables
-load_dotenv()
-SF_USERNAME = os.getenv("SF_USERNAME")
-SF_PASSWORD = os.getenv("SF_PASSWORD")
-SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN")
-
-# Validate Salesforce credentials
-if not all([SF_USERNAME, SF_PASSWORD, SF_SECURITY_TOKEN]):
-    raise ValueError("Missing Salesforce credentials. Set SF_USERNAME, SF_PASSWORD, and SF_SECURITY_TOKEN in environment variables.")
-
-# Initialize Salesforce connection
-try:
-    sf = Salesforce(
-        username=SF_USERNAME,
-        password=SF_PASSWORD,
-        security_token=SF_SECURITY_TOKEN,
-        domain='login'
-    )
-except Exception as e:
-    print(f"Salesforce connection failed: {str(e)}")
-    raise
-
-# Valid milestones
-VALID_MILESTONES = ["Foundation", "Walls Erected", "Planning", "Completed"]
-
 # Adjust the timezone to your local timezone (replace 'Asia/Kolkata' with your timezone if needed)
 local_timezone = pytz.timezone("Asia/Kolkata")
 
-# Deterministic AI prediction with fixed confidence and percent
-def mock_ai_model(image):
-    img = image.convert("RGB")
-    max_size = 1024
-    img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
-
-    img_bytes = img.tobytes()
-    img_hash = int(hashlib.sha256(img_bytes).hexdigest(), 16)
-
-    milestone_index = img_hash % len(VALID_MILESTONES)
-    milestone = VALID_MILESTONES[milestone_index]
-
-    milestone_completion_map = {
-        "Planning": 10,
-        "Foundation": 30,
-        "Walls Erected": 50,
-        "Completed": 100,
-    }
-    completion_percent = milestone_completion_map.get(milestone, 0)
-
-    confidence_raw = 0.85 + ((img_hash % 1000) / 1000) * (0.95 - 0.85)
-    confidence_score = round(confidence_raw, 2)
-
-    return milestone, completion_percent, confidence_score
-
 # Image processing and Salesforce upload
 def process_image(image, project_name):
     try:
@@ -108,14 +50,14 @@ def process_image(image, project_name):
         milestone, percent_complete, confidence_score = mock_ai_model(img)
 
         # Adjust the current time to local timezone
-        local_time = datetime.now(local_timezone).strftime("%Y-%m-%d %H:%M:%S")
+        local_time = datetime.now(local_timezone).strftime("%Y-%m-%dT%H:%M:%SZ")  # Updated format for Salesforce
 
         # Create the Salesforce record with the image URL and AI prediction
         record = {
             "Name__c": project_name,
             "Current_Milestone__c": milestone,
             "Completion_Percentage__c": percent_complete,
-            "Last_Updated_On__c": local_time,  # Use the adjusted local time
+            "Last_Updated_On__c": local_time,  # Correct format for Salesforce
             "Upload_Status__c": "Success",
             "Comments__c": f"{milestone} with {confidence_score*100}% confidence",  # Removed "AI Prediction:"
             "Last_Updated_Image__c": file_url
@@ -137,25 +79,3 @@ def process_image(image, project_name):
 
     except Exception as e:
         return f"Error: {str(e)}", "Failure", "", "", 0
-
-# 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>")
-    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")
-    milestone = gr.Textbox(label="Detected Milestone")
-    confidence = gr.Textbox(label="Confidence Score")
-    progress = gr.Slider(0, 100, label="Completion Percentage", interactive=False, value=0)
-
-    submit_button.click(
-        fn=process_image,
-        inputs=[image_input, project_name_input],
-        outputs=[output_text, upload_status, milestone, confidence, progress]
-    )
-
-demo.launch(share=True)