Update app.py

app.py

@@ -1,3 +1,144 @@
+# import gradio as gr
+# print("GRADIO VERSION:", gr.__version__)
+# import json
+# import os
+# import tempfile
+# from pathlib import Path
+
+# # ==============================
+# # PIPELINE IMPORT
+# # ==============================
+# try:
+#     from working_yolo_pipeline import run_document_pipeline, DEFAULT_LAYOUTLMV3_MODEL_PATH, WEIGHTS_PATH
+# except ImportError:
+#     print("Warning: 'working_yolo_pipeline.py' not found. Using dummy paths.")
+#     def run_document_pipeline(*args):
+#         return {"error": "Placeholder pipeline function called."}
+#     DEFAULT_LAYOUTLMV3_MODEL_PATH = "./models/layoutlmv3_model"
+#     WEIGHTS_PATH = "./weights/yolo_weights.pt"
+
+
+# def process_file(uploaded_file, layoutlmv3_model_path=None):
+#     """
+#     Handles both PDF and Image uploads and routes them to the YOLO/OCR pipeline.
+#     """
+#     if uploaded_file is None:
+#         return "❌ Error: No file uploaded.", None
+
+#     if not layoutlmv3_model_path:
+#         layoutlmv3_model_path = DEFAULT_LAYOUTLMV3_MODEL_PATH
+
+#     if not os.path.exists(layoutlmv3_model_path):
+#         return f"❌ Error: LayoutLMv3 model not found at {layoutlmv3_model_path}", None
+
+#     if not os.path.exists(WEIGHTS_PATH):
+#         return f"❌ Error: YOLO weights not found at {WEIGHTS_PATH}", None
+
+#     try:
+#         file_path = uploaded_file.name
+        
+#         # Determine file type for logging
+#         ext = Path(file_path).suffix.lower()
+#         file_type = "Image" if ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'] else "PDF"
+#         print(f"🚀 Starting pipeline for {file_type}: {file_path}")
+
+#         # Call the pipeline exactly as before. 
+#         # Our modified working_yolo_pipeline now handles the branching internally.
+#         result = run_document_pipeline(file_path, layoutlmv3_model_path)
+
+#         if result is None:
+#             return "❌ Error: Pipeline failed to process the document. Check console for details.", None
+
+#         # Prepare output file for download
+#         output_filename = f"{Path(file_path).stem}_analysis.json"
+#         temp_output = tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json', prefix='analysis_')
+
+#         with open(temp_output.name, 'w', encoding='utf-8') as f:
+#             json.dump(result, f, indent=2, ensure_ascii=False)
+
+#         json_display = json.dumps(result, indent=2, ensure_ascii=False)
+
+#         return json_display, temp_output.name
+
+#     except Exception as e:
+#         return f"❌ Error during processing: {str(e)}", None
+
+
+# # ==============================
+# # GRADIO INTERFACE
+# # ==============================
+# with gr.Blocks(title="Document Analysis Pipeline") as demo:
+
+#     gr.Markdown("""
+#     # 📄 Document & Image Analysis Pipeline
+
+#     Upload a **PDF document** or an **Image (JPG/PNG)** to extract structured data.
+    
+#     **Supported Formats:** `.pdf`, `.jpg`, `.jpeg`, `.png`, `.bmp`, `.webp`
+
+#     **Pipeline Steps:**
+#     1. 🔍 **YOLO/OCR**: Word extraction + Figure/Equation detection
+#     2. 🤖 **LayoutLMv3**: BIO tagging and structural analysis
+#     3. 📊 **Decoding**: Conversion to hierarchical JSON
+#     4. 🖼️ **Extraction**: Base64 embedding of detected visual elements
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             file_input = gr.File(
+#                 label="Upload PDF or Image",
+#                 file_types=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"],
+#                 type="filepath"
+#             )
+
+#             model_path_input = gr.Textbox(
+#                 label="LayoutLMv3 Model Path (optional)",
+#                 placeholder=DEFAULT_LAYOUTLMV3_MODEL_PATH,
+#                 value=DEFAULT_LAYOUTLMV3_MODEL_PATH,
+#                 interactive=True
+#             )
+
+#             process_btn = gr.Button("🚀 Process File", variant="primary", size="lg")
+
+#             gr.Markdown("""
+#             ### ℹ️ Notes:
+#             - **Images** are treated as single-page documents.
+#             - **PDFs** are processed page-by-page.
+#             - High-resolution Tesseract OCR is used for all image content.
+#             """)
+
+#         with gr.Column(scale=2):
+#             json_output = gr.Code(
+#                 label="Structured JSON Output",
+#                 language="json",
+#                 lines=25
+#             )
+
+#             download_output = gr.File(
+#                 label="Download Full JSON",
+#                 interactive=False
+#             )
+
+#     # UI Logic
+#     process_btn.click(
+#         fn=process_file,
+#         inputs=[file_input, model_path_input],
+#         outputs=[json_output, download_output],
+#         api_name="process_document"
+#     )
+
+# if __name__ == "__main__":
+#     demo.launch(
+#         server_name="0.0.0.0",
+#         server_port=7860,
+#         share=False,
+#         show_error=True
+#     )
+
+
+
+
+
 import gradio as gr
 print("GRADIO VERSION:", gr.__version__)
 import json
@@ -25,6 +166,24 @@ def process_file(uploaded_file, layoutlmv3_model_path=None):
     if uploaded_file is None:
         return "❌ Error: No file uploaded.", None
 
+    # --- FIX FOR GRADIO 6.x FILE HANDLING ---
+    # If multiple files were somehow uploaded or Gradio returned a list
+    if isinstance(uploaded_file, list):
+        uploaded_file = uploaded_file[0]
+    
+    # Extract the actual file path string. 
+    # Gradio File objects have a '.path' attribute for the temporary local location.
+    try:
+        if hasattr(uploaded_file, 'path'):
+            file_path = uploaded_file.path
+        elif isinstance(uploaded_file, dict):
+            file_path = uploaded_file.get("path")
+        else:
+            file_path = str(uploaded_file)
+    except Exception as e:
+        return f"❌ Error resolving file path: {str(e)}", None
+    # ---------------------------------------
+
     if not layoutlmv3_model_path:
         layoutlmv3_model_path = DEFAULT_LAYOUTLMV3_MODEL_PATH
 
@@ -35,15 +194,12 @@ def process_file(uploaded_file, layoutlmv3_model_path=None):
         return f"❌ Error: YOLO weights not found at {WEIGHTS_PATH}", None
 
     try:
-        file_path = uploaded_file.name
-        
-        # Determine file type for logging
+        # Determine file type for logging safely
         ext = Path(file_path).suffix.lower()
         file_type = "Image" if ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'] else "PDF"
         print(f"🚀 Starting pipeline for {file_type}: {file_path}")
 
-        # Call the pipeline exactly as before. 
-        # Our modified working_yolo_pipeline now handles the branching internally.
+        # Call the pipeline
         result = run_document_pipeline(file_path, layoutlmv3_model_path)
 
         if result is None:
@@ -61,6 +217,9 @@ def process_file(uploaded_file, layoutlmv3_model_path=None):
         return json_display, temp_output.name
 
     except Exception as e:
+        # This is where your previous error message was being caught and returned
+        import traceback
+        traceback.print_exc() # This prints the full error to your terminal for debugging
         return f"❌ Error during processing: {str(e)}", None
 
 
@@ -71,16 +230,7 @@ with gr.Blocks(title="Document Analysis Pipeline") as demo:
 
     gr.Markdown("""
     # 📄 Document & Image Analysis Pipeline
-
     Upload a **PDF document** or an **Image (JPG/PNG)** to extract structured data.
-    
-    **Supported Formats:** `.pdf`, `.jpg`, `.jpeg`, `.png`, `.bmp`, `.webp`
-
-    **Pipeline Steps:**
-    1. 🔍 **YOLO/OCR**: Word extraction + Figure/Equation detection
-    2. 🤖 **LayoutLMv3**: BIO tagging and structural analysis
-    3. 📊 **Decoding**: Conversion to hierarchical JSON
-    4. 🖼️ **Extraction**: Base64 embedding of detected visual elements
     """)
 
     with gr.Row():
@@ -88,7 +238,8 @@ with gr.Blocks(title="Document Analysis Pipeline") as demo:
             file_input = gr.File(
                 label="Upload PDF or Image",
                 file_types=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"],
-                type="filepath"
+                type="filepath",
+                file_count="single"  # Force single file to avoid list/tuple issues
             )
 
             model_path_input = gr.Textbox(
@@ -100,13 +251,6 @@ with gr.Blocks(title="Document Analysis Pipeline") as demo:
 
             process_btn = gr.Button("🚀 Process File", variant="primary", size="lg")
 
-            gr.Markdown("""
-            ### ℹ️ Notes:
-            - **Images** are treated as single-page documents.
-            - **PDFs** are processed page-by-page.
-            - High-resolution Tesseract OCR is used for all image content.
-            """)
-
         with gr.Column(scale=2):
             json_output = gr.Code(
                 label="Structured JSON Output",
@@ -133,4 +277,4 @@ if __name__ == "__main__":
         server_port=7860,
         share=False,
         show_error=True
-    )
+    )