Update app.py

app.py

@@ -1,3 +1,154 @@
+# import gradio as gr
+# import json
+# import os
+# import tempfile
+# import img2pdf
+# 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_files, layoutlmv3_model_path=None):
+#     """
+#     Converts multiple images into a single PDF (if necessary) and routes 
+#     the result to the YOLO/OCR pipeline as a single entity.
+#     """
+#     if not uploaded_files:
+#         return "❌ Error: No files uploaded.", None
+
+#     # Ensure we are working with a list of files (Gradio file_count="multiple" returns a list)
+#     if not isinstance(uploaded_files, list):
+#         uploaded_files = [uploaded_files]
+
+#     # 1. Resolve all file paths
+#     resolved_paths = []
+#     for f in uploaded_files:
+#         if hasattr(f, 'path'):
+#             resolved_paths.append(f.path)
+#         elif isinstance(f, dict):
+#             resolved_paths.append(f.get("path"))
+#         else:
+#             resolved_paths.append(str(f))
+
+#     # 2. Determine if we should merge into a single PDF
+#     # We merge if there are multiple files OR if the single file is an image
+#     first_file = Path(resolved_paths[0])
+#     is_image = first_file.suffix.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.webp', '.tiff']
+    
+#     processing_path = None
+    
+#     try:
+#         if len(resolved_paths) > 1 or (len(resolved_paths) == 1 and is_image):
+#             print(f"📦 Converting {len(resolved_paths)} image(s) to a single PDF entity...")
+#             temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+#             # img2pdf.convert converts a list of image paths into PDF bytes
+#             with open(temp_pdf.name, "wb") as f:
+#                 f.write(img2pdf.convert(resolved_paths))
+#             processing_path = temp_pdf.name
+#         else:
+#             # It's a single PDF, process directly
+#             processing_path = resolved_paths[0]
+
+#         # 3. Standard Pipeline Checks
+#         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
+
+#         print(f"🚀 Starting pipeline for merged entity: {processing_path}")
+
+#         # 4. Call the pipeline
+#         result = run_document_pipeline(processing_path, layoutlmv3_model_path)
+
+#         if result is None:
+#             return "❌ Error: Pipeline failed to process the document.", None
+
+#         # 5. Prepare output
+#         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:
+#         import traceback
+#         traceback.print_exc()
+#         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 **multiple images** or a **PDF**. Multiple images will be processed together as a single continuous document.
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             file_input = gr.File(
+#                 label="Upload PDFs or Images",
+#                 file_types=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"],
+#                 type="filepath",
+#                 file_count="multiple"  # ALLOWS MULTIPLE FILES
+#             )
+
+#             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 Files", variant="primary", size="lg")
+
+#         with gr.Column(scale=2):
+#             json_output = gr.Code(
+#                 label="Combined 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
 import json
 import os
@@ -19,128 +170,111 @@ except ImportError:
 
 def process_file(uploaded_files, layoutlmv3_model_path=None):
     """
-    Converts multiple images into a single PDF (if necessary) and routes 
-    the result to the YOLO/OCR pipeline as a single entity.
+    Robust handler for multiple or single file uploads.
     """
-    if not uploaded_files:
+    if uploaded_files is None:
         return "❌ Error: No files uploaded.", None
 
-    # Ensure we are working with a list of files (Gradio file_count="multiple" returns a list)
+    # --- THE ROBUST FIX ---
+    # Gradio sometimes sends a single dict even when set to multiple.
+    # We force everything into a list so the rest of the logic doesn't break.
     if not isinstance(uploaded_files, list):
-        uploaded_files = [uploaded_files]
+        file_list = [uploaded_files]
+    else:
+        file_list = uploaded_files
 
-    # 1. Resolve all file paths
+    if len(file_list) == 0:
+        return "❌ Error: Empty file list.", None
+    # ----------------------
+
+    # 1. Resolve all file paths safely
     resolved_paths = []
-    for f in uploaded_files:
-        if hasattr(f, 'path'):
-            resolved_paths.append(f.path)
-        elif isinstance(f, dict):
-            resolved_paths.append(f.get("path"))
-        else:
-            resolved_paths.append(str(f))
+    for f in file_list:
+        try:
+            if isinstance(f, dict) and "path" in f:
+                resolved_paths.append(f["path"])
+            elif hasattr(f, 'path'):
+                resolved_paths.append(f.path)
+            else:
+                resolved_paths.append(str(f))
+        except Exception as e:
+            print(f"Error resolving path for {f}: {e}")
+
+    if not resolved_paths:
+        return "❌ Error: Could not resolve file paths.", None
 
     # 2. Determine if we should merge into a single PDF
-    # We merge if there are multiple files OR if the single file is an image
     first_file = Path(resolved_paths[0])
     is_image = first_file.suffix.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.webp', '.tiff']
     
-    processing_path = None
-    
     try:
-        if len(resolved_paths) > 1 or (len(resolved_paths) == 1 and is_image):
-            print(f"📦 Converting {len(resolved_paths)} image(s) to a single PDF entity...")
+        # If it's multiple files or just one image, wrap it in a PDF
+        if len(resolved_paths) > 1 or is_image:
+            print(f"📦 Converting {len(resolved_paths)} image(s) to a single PDF...")
             temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
-            # img2pdf.convert converts a list of image paths into PDF bytes
-            with open(temp_pdf.name, "wb") as f:
-                f.write(img2pdf.convert(resolved_paths))
+            with open(temp_pdf.name, "wb") as f_out:
+                f_out.write(img2pdf.convert(resolved_paths))
             processing_path = temp_pdf.name
         else:
-            # It's a single PDF, process directly
+            # It's a single PDF
             processing_path = resolved_paths[0]
 
         # 3. Standard Pipeline Checks
-        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
-
-        print(f"🚀 Starting pipeline for merged entity: {processing_path}")
+        final_model_path = layoutlmv3_model_path or DEFAULT_LAYOUTLMV3_MODEL_PATH
+        if not os.path.exists(final_model_path):
+            return f"❌ Error: Model not found at {final_model_path}", None
 
         # 4. Call the pipeline
-        result = run_document_pipeline(processing_path, layoutlmv3_model_path)
+        print(f"🚀 Starting pipeline for: {processing_path}")
+        result = run_document_pipeline(processing_path, final_model_path)
 
         if result is None:
-            return "❌ Error: Pipeline failed to process the document.", None
+            return "❌ Error: Pipeline returned None.", None
 
         # 5. Prepare output
         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
+        return json.dumps(result, indent=2, ensure_ascii=False), temp_output.name
 
     except Exception as e:
         import traceback
         traceback.print_exc()
-        return f"❌ Error during processing: {str(e)}", None
+        return f"❌ Error: {str(e)}", None
 
 # ==============================
 # GRADIO INTERFACE
 # ==============================
 with gr.Blocks(title="Document Analysis Pipeline") as demo:
 
-    gr.Markdown("""
-    # 📄 Document & Image Analysis Pipeline
-    Upload **multiple images** or a **PDF**. Multiple images will be processed together as a single continuous document.
-    """)
+    gr.Markdown("# 📄 Document & Image Analysis Pipeline")
 
     with gr.Row():
         with gr.Column(scale=1):
             file_input = gr.File(
                 label="Upload PDFs or Images",
                 file_types=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"],
-                type="filepath",
-                file_count="multiple"  # ALLOWS MULTIPLE FILES
+                file_count="multiple", # Keep this
+                type="filepath"       # Keep this
             )
 
             model_path_input = gr.Textbox(
-                label="LayoutLMv3 Model Path (optional)",
-                placeholder=DEFAULT_LAYOUTLMV3_MODEL_PATH,
-                value=DEFAULT_LAYOUTLMV3_MODEL_PATH,
-                interactive=True
+                label="Model Path",
+                value=DEFAULT_LAYOUTLMV3_MODEL_PATH
             )
 
-            process_btn = gr.Button("🚀 Process Files", variant="primary", size="lg")
+            process_btn = gr.Button("🚀 Process Files", variant="primary")
 
         with gr.Column(scale=2):
-            json_output = gr.Code(
-                label="Combined Structured JSON Output",
-                language="json",
-                lines=25
-            )
-
-            download_output = gr.File(
-                label="Download Full JSON",
-                interactive=False
-            )
+            json_output = gr.Code(label="JSON Output", language="json", lines=20)
+            download_output = gr.File(label="Download JSON")
 
-    # UI Logic
     process_btn.click(
         fn=process_file,
         inputs=[file_input, model_path_input],
-        outputs=[json_output, download_output],
-        api_name="process_document"
+        outputs=[json_output, download_output]
     )
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)