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"""
Upload documents tab functionality for the Gradio app
"""
import gradio as gr
def upload_documents(files, global_vars):
"""Handle document upload and processing"""
doc_ingestion = global_vars.get('doc_ingestion')
if not doc_ingestion:
return "❌ Please initialize systems first using the 'Initialize System' tab!"
if not files:
return "❌ Please upload at least one PDF file."
try:
# Filter for PDF files only
pdf_files = []
for file_path in files:
if file_path.endswith('.pdf'):
pdf_files.append(file_path)
if not pdf_files:
return "❌ Please upload PDF files only."
print(f"📄 Processing {len(pdf_files)} PDF file(s)...")
# Process documents
documents = doc_ingestion.process_documents(pdf_files)
if documents:
print("🔗 Creating vector store...")
# Create vector store
vectorstore = doc_ingestion.create_vector_store(documents)
if vectorstore:
# Store vectorstore in global vars
global_vars['vectorstore'] = vectorstore
# Create summary
summary = f"✅ Successfully processed {len(documents)} document(s):\n\n"
for i, doc in enumerate(documents, 1):
metadata = doc.metadata
university = metadata.get('university', 'Unknown')
country = metadata.get('country', 'Unknown')
doc_type = metadata.get('document_type', 'Unknown')
language = metadata.get('language', 'Unknown')
summary += f"{i}. **{metadata['source']}**\n"
summary += f" - University: {university}\n"
summary += f" - Country: {country}\n"
summary += f" - Type: {doc_type}\n"
summary += f" - Language: {language}\n\n"
summary += "🎉 **Ready for queries!** Go to the 'Search & Query' tab to start asking questions."
return summary
else:
return "❌ Failed to create vector store from documents."
else:
return "❌ No documents were successfully processed. Please check if your PDFs are readable."
except Exception as e:
return f"❌ Error processing documents: {str(e)}\n\nPlease check the console for more details."
def create_upload_tab(global_vars):
"""Create the Upload Documents tab"""
with gr.Tab("📄 Upload Documents", id="upload"):
gr.Markdown("""
### Step 2: Upload PDF Documents
Upload university documents (brochures, admission guides, etc.) in PDF format.
The system will automatically extract metadata including university name, country, and document type.
""")
file_upload = gr.File(
label="📁 Upload PDF Documents",
file_types=[".pdf"],
file_count="multiple",
height=120
)
upload_btn = gr.Button(
"📄 Process Documents",
variant="primary",
size="lg"
)
upload_status = gr.Textbox(
label="Processing Status",
interactive=False,
lines=12,
placeholder="Upload PDF files and click 'Process Documents'..."
)
upload_btn.click(
lambda files: upload_documents(files, global_vars),
inputs=file_upload,
outputs=upload_status
)
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