Update app.py

app.py

@@ -1,7 +1,10 @@
 import os
+import json
 import gradio as gr
 import pandas as pd
-import time
+from python_request import process_wod_document
+
+from dummy import output_test
 
 # --- Authentication Function ---
 def authenticate_user(username, password):
@@ -14,9 +17,7 @@ def authenticate_user(username, password):
 # --- Core Application Logic ---
 def analyze_wod(file_obj, wod_type):
     """
-    This function simulates the analysis of a Work Order Document.
-    In a real application, this is where you would put your PDF parsing,
-    text extraction, and validation logic.
+    This function analyzes a Work Order Document using the remote API.
 
     Args:
         file_obj: The uploaded file object from Gradio.
@@ -25,59 +26,84 @@ def analyze_wod(file_obj, wod_type):
     Returns:
         A pandas DataFrame with the analysis results.
     """
-    # Print to console to show the function is running
-    if file_obj is not None:
-        print(f"Analyzing '{file_obj.name}' (Type: {wod_type})...")
-    else:
-        print(f"Analyzing with no file uploaded (Type: {wod_type})...")
-
-    # Simulate a processing delay
-    time.sleep(2)
-
-    # --- Dummy Data Generation ---
-    # This data simulates the results you would get from a real analysis.
-    # We include a "Fail" case to demonstrate how it would look.
-    data = {
-        "Requirement": [
-            "Merchant Front Photo",
-            "EDC Component Photo",
-            "EDC Placement Photo",
-            "Terminal Data Verification",
-            "Timestamped Photos Required",
-            "Clear Sales Draft",
-            "Date Consistency Check",
-            "PIC Signature/Handover",
-            "BAST/Handover Document",
-        ],
-        "Reason / Location": [
-            "Available on Page 2 of the submission.",
-            "Found in Attachment 1, Photo A.",
-            "Available on Page 2, second photo.",
-            "Confirmed on the test transaction receipt, Page 3.",
-            "Timestamps are visible on all photos on Page 2.",
-            "Document not found; attachment is missing.",
-            "Dates on the cover sheet and Page 3 match.",
-            "Signature is on the Handover Form, Page 4.",
-            "The BAST is signed and available on Page 4.",
-        ],
-        "Status": [
-            "PASS",
-            "PASS",
-            "PASS",
-            "PASS",
-            "PASS",
-            "FAIL",
-            "PASS",
-            "PASS",
-            "PASS",
-        ],
-    }
+    # Check if user has selected a valid WOD type
+    if wod_type == "-- WOD type --" or wod_type is None:
+        # Show warning dialog and return empty DataFrame
+        gr.Warning("Please select a WOD type first!")
+        return pd.DataFrame()
     
-    # Create a pandas DataFrame from the dummy data
-    df = pd.DataFrame(data)
+    # Check if file is uploaded
+    if file_obj is None:
+        gr.Warning("Please upload a PDF file first!")
+        return pd.DataFrame()
     
-    # Return the DataFrame to be displayed in the Gradio interface
-    return df
+    print(f"Analyzing '{file_obj.name}' (Type: {wod_type})...")
+    
+    try:
+        # In modern Gradio versions, file_obj is already a path string
+        # We can use it directly or get the path from it
+        if hasattr(file_obj, 'name') and os.path.isfile(file_obj.name):
+            # file_obj has a .name attribute pointing to the temporary file
+            temp_file_path = file_obj.name
+            cleanup_needed = False
+        else:
+            # Fallback: assume file_obj is a path string
+            temp_file_path = str(file_obj)
+            cleanup_needed = False
+        
+        # Process the document using the API
+        #api_response = process_wod_document(temp_file_path, wod_type)
+
+        api_response = json.loads(output_test) 
+        
+        # Clean up temporary file if we created one
+        if cleanup_needed:
+            os.unlink(temp_file_path)
+        
+        # Check if API call was successful
+        if api_response.get("status") != "success":
+            error_msg = api_response.get("message", "Unknown error occurred")
+            gr.Error(f"API Error: {error_msg}")
+            return pd.DataFrame()
+        
+        # Parse the API response
+        results = api_response.get("results", {})
+        summary = results.get("summary", {})
+        
+        # Convert API response to DataFrame format
+        requirements = []
+        reasons = []
+        statuses = []
+        
+        for requirement_name, details in summary.items():
+            requirements.append(requirement_name)
+            reasons.append(details.get("reasoning", ""))
+            # Convert true/false to PASS/FAIL
+            status_bool = details.get("status", "false")
+            if isinstance(status_bool, str):
+                status = "PASS" if status_bool.lower() == "true" else "FAIL"
+            else:
+                status = "PASS" if status_bool else "FAIL"
+            statuses.append(status)
+        
+        # Create DataFrame
+        df = pd.DataFrame({
+            "Requirement": requirements,
+            "Reason": reasons,
+            "Status": statuses
+        })
+        
+        # Show success message with prediction
+        prediction = results.get("prediction", "Unknown")
+        gr.Info(f"Analysis completed! Overall prediction: {prediction}")
+        
+        return df
+        
+    except Exception as e:
+        error_msg = f"Error processing document: {str(e)}"
+        print(error_msg)
+        gr.Error(error_msg)
+        return pd.DataFrame()
 
 # --- Gradio User Interface Definition ---
 # Using gr.Blocks() for a custom layout that matches the elegant design.
@@ -102,9 +128,9 @@ with gr.Blocks(
         
         # Dropdown for WOD Type
         type_input = gr.Dropdown(
-            ["REPLACEMENT", "THERMAL", "VISIT", "PREVENTIVE_MAINTENANCE", "INSTALLATION", "WITHDRAWAL"],
+            ["-- WOD type --", "REPLACEMENT", "THERMAL", "VISIT", "PREVENTIVE_MAINTENANCE", "INSTALLATION", "WITHDRAWAL"],
             label="Type",
-            value="REPLACEMENT",
+            value="-- WOD type --",
             info="Select the type of work order."
         )
 
@@ -117,12 +143,12 @@ with gr.Blocks(
     
     # DataFrame to display the output, with styling for the 'Status' column
     results_output = gr.DataFrame(
-        headers=["Requirement", "Reason / Location", "Status"],
+        headers=["Requirement", "Reason", "Status"],
         datatype=["str", "str", "str"],
-        # This part styles the 'Status' column based on its value
-        # It applies a green background for 'PASS' and a red one for 'FAIL'
         interactive=False,
-        col_count=(3, "fixed"),
+        max_height=1250,
+        column_widths=[30, 60, 10],
+        wrap=True
     )
 
     # Define the interaction: clicking the button calls the function
@@ -136,6 +162,9 @@ with gr.Blocks(
 if __name__ == "__main__":
     # The launch() command creates a web server with authentication enabled
     # Users must provide the correct username and password to access the app
+
+    # demo.launch(debug=True)
+
     demo.launch(
         auth=authenticate_user,  # Enable authentication
         auth_message="Please enter your credentials to access the WOD Analyzer",