Update app.py

app.py

@@ -189,10 +189,14 @@ def process_query(query: str, first_turn: bool = False):
 
     _save_process_log(process_log)
     return intro + result
+
+def main_pipeline_with_doc_and_history(query, doc_file, doc_type, history):
+    response = main_pipeline_with_doc(query, doc_file, doc_type)
+    updated_history = history + f"\nUser: {query}\nWisal: {response}\n"
+    return response, updated_history
+
 # Utility to save process log to a txt file
 def _save_process_log(log_lines, filename="process_output.txt"):
-    import datetime
-    import os
     # Ensure logs directory exists
     logs_dir = os.path.join(os.path.dirname(__file__), "logs")
     os.makedirs(logs_dir, exist_ok=True)
@@ -254,19 +258,35 @@ def main_pipeline_with_doc(query, doc_file, doc_type):
         return "Invalid document type."
 
 with gr.Blocks(title="Wisal Main Pipeline & RAG") as demo:
-    gr.Markdown("## Wisal: Autism AI Assistant (Main Pipeline)")
-    with gr.Tab("Main Pipeline"):
-        q = gr.Textbox(placeholder="Your question...", lines=2, label="Ask Wisal")
-        doc_file = gr.File(label="Optional: Upload Document (PDF, DOCX, TXT)")
-        doc_type = gr.Radio(["None", "Knowledge Document", "User-Specific Document", "Old Document"], value="None", label="Document Type")
-        btn = gr.Button("Submit")
-        out = gr.Textbox(label="Wisal Answer", lines=8, interactive=False)
-        btn.click(fn=main_pipeline_with_doc, inputs=[q, doc_file, doc_type], outputs=out)
-    with gr.Tab("Domain Knowledge RAG"):
+    gr.Markdown("## 🤖 Wisal: Autism AI Assistant")
+
+    with gr.Tab("🧠 Main Chatbot"):
+        with gr.Column():
+            history = gr.Textbox(label="🗨️ Chat History", lines=20, interactive=False, show_label=False)
+            
+            with gr.Row():
+                q = gr.Textbox(placeholder="Type your question here...", label="", lines=1)
+                btn = gr.Button("Send", variant="primary")
+
+            with gr.Row():
+                doc_file = gr.File(label="📎 Upload Document (PDF, DOCX, TXT)", file_types=[".pdf", ".docx", ".txt"])
+                doc_type = gr.Radio(
+                    ["None", "Knowledge Document", "User-Specific Document", "Old Document"],
+                    value="None",
+                    label="Document Type"
+                )
+
+            btn.click(
+                fn=main_pipeline_with_doc_and_history,
+                inputs=[q, doc_file, doc_type, history],
+                outputs=[history]
+            )
+
+    with gr.Tab("📘 Domain Knowledge RAG"):
         RAG_Domain_know_doc.demo.render()
-    with gr.Tab("User-Specific Documents"):
+    with gr.Tab("📁 User-Specific Documents"):
         User_Specific_Documents.demo.render()
-    with gr.Tab("Old Documents"):
+    with gr.Tab("🕰️ Old Documents"):
         Old_Document.demo.render()
 
 if __name__ == "__main__":