Update app.py

app.py

@@ -8,6 +8,7 @@ import gradio as gr
 import gdown
 from datetime import datetime
 from pathlib import Path
+import zipfile
 
 load_dotenv(override=True)
 
@@ -38,9 +39,9 @@ record_user_details_json = {
     "parameters": {
         "type": "object",
         "properties": {
-            "email": {"type": "string", "description": "Their email address"},
-            "name": {"type": "string", "description": "Their name"},
-            "notes": {"type": "string", "description": "Conversation context"}
+            "email": {"type": "string"},
+            "name": {"type": "string"},
+            "notes": {"type": "string"}
         },
         "required": ["email"],
         "additionalProperties": False
@@ -49,11 +50,11 @@ record_user_details_json = {
 
 record_unknown_question_json = {
     "name": "record_unknown_question",
-    "description": "Record questions that couldn't be answered",
+    "description": "Record a question that couldn't be answered",
     "parameters": {
         "type": "object",
         "properties": {
-            "question": {"type": "string", "description": "The unknown question"},
+            "question": {"type": "string"}
         },
         "required": ["question"],
         "additionalProperties": False
@@ -73,17 +74,17 @@ class Me:
         Path("chat_logs").mkdir(exist_ok=True)
 
         # Download LinkedIn PDF
-        linkedin_id = "1xz2RowkImpI8odYv8zvKdlRHaKfILn40"
-        gdown.download(f"https://drive.google.com/uc?id={linkedin_id}", "linkedin.pdf", quiet=False)
+        gdown.download("https://drive.google.com/uc?id=1xz2RowkImpI8odYv8zvKdlRHaKfILn40", "linkedin.pdf", quiet=False)
         reader = PdfReader("linkedin.pdf")
         self.linkedin = "".join(page.extract_text() or "" for page in reader.pages)
 
         # Download summary.txt
-        summary_id = "1hjJz082YFSVjFtpO0pwT6Tyy3eLYYj6-"
-        gdown.download(f"https://drive.google.com/uc?id={summary_id}", "summary.txt", quiet=False)
+        gdown.download("https://drive.google.com/uc?id=1hjJz082YFSVjFtpO0pwT6Tyy3eLYYj6-", "summary.txt", quiet=False)
         with open("summary.txt", "r", encoding="utf-8") as f:
             self.summary = f.read()
 
+        self.archive_logs()
+
     def system_prompt(self):
         return f"""You are acting as {self.name}. You're answering questions on {self.name}'s website about his career, experience, and skills.
 Be professional and conversational, as if talking to a potential employer or client.
@@ -130,6 +131,9 @@ If you can't answer something, call `record_unknown_question`. If a user seems i
                 full_response += delta.content
                 yield full_response
 
+        # Append a helpful follow-up message
+        full_response += "\n\n💬 Let me know if you’d like to follow up or need help connecting with Jacob."
+
         self.session_log.append({"role": "assistant", "content": full_response})
         self.save_session_log()
 
@@ -139,18 +143,27 @@ If you can't answer something, call `record_unknown_question`. If a user seems i
         with open(filename, "w", encoding="utf-8") as f:
             json.dump(self.session_log, f, indent=2)
 
+    def archive_logs(self):
+        """Create a zip archive of all previous logs — replaceable for weekly automation"""
+        zip_path = "chat_logs/weekly_archive.zip"
+        with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as archive:
+            for log_file in Path("chat_logs").glob("session_*.json"):
+                archive.write(log_file, arcname=log_file.name)
+
+# Instantiate assistant
 me = Me()
 
+# Build UI
 with gr.Blocks(title="Jacob Isaacson Chatbot") as iface:
     with gr.Row():
-        gr.Image("jacob.png", width=120, show_label=False)
-        gr.Markdown("### Chat with Jacob Isaacson\nAsk about Jacob's background, skills, or projects. 🛡️ *Chat is logged.*")
+        gr.Image("jacob.png", width=100, show_label=False)
+        gr.Markdown("### Chat with Jacob Isaacson\nAsk about Jacob's background, skills, or career. \n🛡️ *All chats are logged for improvement purposes.*")
 
     gr.ChatInterface(
         fn=me.chat_stream,
         chatbot=gr.Chatbot(show_copy_button=True),
         examples=["What is Jacob's experience with AI?", "Tell me about his recent projects."],
-        type="messages"  # ✅ OpenAI-style message format
+        type="messages"
     )
 
 if __name__ == "__main__":