Update app.py

app.py

@@ -1,3 +1,13 @@
+import gradio as gr
+import requests
+from PIL import Image
+import io
+import os
+import re
+from dotenv import load_dotenv  # Fixed: Added this import
+from simple_salesforce import Salesforce
+from datetime import datetime
+
 # Load environment variables
 load_dotenv()
 HF_API_KEY = os.getenv("HF_API_KEY")
@@ -23,4 +33,121 @@ try:
     )
 except Exception as e:
     print(f"Salesforce connection failed: {str(e)}")
-    raise
+    raise
+
+# Hugging Face Inference API endpoint (replace with your model)
+HF_MODEL_URL = "https://api-inference.huggingface.co/models/nasreshsuguru/construction-milestone-detector"
+
+def process_image(image, project_name):
+    try:
+        # Validate inputs
+        if image is None:
+            return "Error: Please upload an image to proceed.", "Pending", "", 0.0, 0.0, ""
+        if not project_name:
+            return "Error: Please enter a project name to proceed.", "Pending", "", 0.0, 0.0, ""
+        if not re.match(r'^[a-zA-Z0-9\s-]+$', project_name):
+            return "Error: Project name must be alphanumeric (letters, numbers, spaces, or hyphens).", "Pending", "", 0.0, 0.0, ""
+
+        # Validate image size and type
+        image_size_mb = os.path.getsize(image) / (1024 * 1024)
+        if image_size_mb > 20:
+            return "Error: Image size exceeds 20MB.", "Failure", "", 0.0, 0.0, ""
+        if not image.lower().endswith(('.jpg', '.jpeg', '.png')):
+            return "Error: Only JPG/PNG images are supported.", "Failure", "", 0.0, 0.0, ""
+
+        # Preprocess image
+        img = Image.open(image)
+        max_size = 1024  # Optimize for performance
+        img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
+        img = img.resize((224, 224))
+        img_byte_arr = io.BytesIO()
+        img.save(img_byte_arr, format="PNG")
+        img_byte_arr = img_byte_arr.getvalue()
+
+        # Call Hugging Face API
+        headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+        response = requests.post(HF_MODEL_URL, headers=headers, data=img_byte_arr, timeout=10)
+
+        if response.status_code != 200:
+            return f"Error: Hugging Face API failed with status {response.status_code}", "Failure", "", 0.0, 0.0, ""
+
+        # Parse AI output
+        result = response.json()
+        top_predictions = sorted(result, key=lambda x: x["score"], reverse=True)[:3]
+        milestone = top_predictions[0]["label"]
+        confidence = top_predictions[0]["score"]
+        percent_complete = min(max(int(confidence * 100), 0), 100)
+        prediction_details = "\n".join([f"{pred['label']}: {pred['score']:.2f}" for pred in top_predictions])
+
+        # Update Salesforce record
+        record = {
+            "Name": 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 Confidence: {confidence:.2f}",
+            "Version__c": 1
+        }
+
+        try:
+            project_name = project_name.replace("'", "''")  # Basic escaping
+            query = f"SELECT Id, Version__c FROM Construction_Project__c WHERE Name = '{project_name}'"
+            result = sf.query(query)
+            if result["totalSize"] > 0:
+                project_id = result["records"][0]["Id"]
+                current_version = result["records"][0].get("Version__c", 0)
+                record["Version__c"] = current_version + 1
+                sf.Construction_Project__c.update(project_id, record)
+            else:
+                sf.Construction_Project__c.create(record)
+        except Exception as e:
+            return f"Error: Failed to update Salesforce - {str(e)}", "Failure", "", 0.0, 0.0, prediction_details
+
+        return (
+            f"Success: Milestone: {milestone}, Completion: {percent_complete}%",
+            "Success",
+            milestone,
+            percent_complete,
+            confidence,
+            prediction_details
+        )
+
+    except Exception as e:
+        return f"Error: {str(e)}", "Failure", "", 0.0, 0.0, ""
+
+# Gradio interface
+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>")
+    project_name = gr.Textbox(label="Project Name", placeholder="Enter project name")
+    image_input = gr.Image(type="filepath", label="Upload Construction Site Photo (JPG/PNG, ≤ 20MB)")
+    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")
+    completion_percent = gr.Slider(0, 100, label="Completion Percentage (%)", interactive=False)
+    confidence_score = gr.Slider(0, 1, label="Confidence Score", interactive=False)
+    prediction_details = gr.Textbox(label="Top Predictions")
+    progress = gr.Slider(0, 100, label="Processing Progress", interactive=False, value=0)
+
+    def update_progress():
+        return 50
+
+    def complete_progress():
+        return 100
+
+    submit_button.click(
+        fn=update_progress,
+        outputs=progress
+    ).then(
+        fn=process_image,
+        inputs=[image_input, project_name],
+        outputs=[output_text, upload_status, milestone, completion_percent, confidence_score, prediction_details]
+    ).then(
+        fn=complete_progress,
+        outputs=progress
+    )
+
+if __name__ == "__main__":
+    demo.launch()
+