Update app.py

app.py

@@ -574,165 +574,94 @@ def youtube_search(query, max_results=10):
 # =======================================
 
 def run_playwright_action(action_data, prompt_generator, target_url):
-    """
-    Executes real Playwright actions while sending visual signals 
-    to the chat stream generator.
-    
-    Args:
-        action_data (list): List of LLM-generated actions.
-        prompt_generator (generator): Generator used to stream data back to the frontend.
-        target_url (str): The URL to be visited.
-    """
     print(f"[AGENT] 🚀 Starting Playwright Automation on: {target_url}")
-    
-    # Helper function to send signals to the frontend via the generator
+
+    # Generator pengirim signal
     def send_frontend_signal(action, selector=None, text=""):
         signal = {"agent_action": action, "selector": selector, "text": text}
-        # Sending the signal as a JSON line in the stream
-        prompt_generator.send(f"data: {json.dumps(signal)}\n\n")
+        yield f"data: {json.dumps(signal)}\n\n"
         time.sleep(0.05)
-        
+
     browser = None
+
     try:
         with sync_playwright() as p:
-            # Use 'headless=True' in production
-            browser = p.chromium.launch() 
+            browser = p.chromium.launch()
             page = browser.new_page()
 
-            send_frontend_signal("start_visual_automation", "body", f"Visiting {target_url}...")
-            page.goto(target_url, wait_until="domcontentloaded") 
+            # ⬅️ HARUS pakai yield from
+            yield from send_frontend_signal("start_visual_automation", "body", f"Visiting {target_url}...")
+
+            page.goto(target_url, wait_until="domcontentloaded")
             page.wait_for_selector("body", timeout=10000)
             time.sleep(1)
 
-            # --- Real Automation Steps ---
+            # === REAL ACTIONS ===
             for step in action_data:
                 action_type = step["action"]
                 selector = step.get("selector")
                 text = step.get("text", "")
-                
+
                 print(f"[AGENT] Executing: {action_type} on {selector or 'N/A'}")
 
                 if action_type == "click":
-                    send_frontend_signal("start_visual_automation", selector, f"Clicking {selector}...")
-                    page.wait_for_selector(selector, timeout=10000) 
+                    yield from send_frontend_signal("start_visual_automation", selector, f"Clicking {selector}...")
+                    page.wait_for_selector(selector, timeout=10000)
                     page.click(selector)
-                    send_frontend_signal("click", selector)
+                    yield from send_frontend_signal("click", selector)
                     time.sleep(2)
 
                 elif action_type == "type_text":
-                    send_frontend_signal("start_visual_automation", selector, f"Typing text: '{text[:20]}...'")
+                    yield from send_frontend_signal("start_visual_automation", selector, f"Typing '{text[:20]}...'")
                     page.wait_for_selector(selector, timeout=10000)
-                    page.fill(selector, "") 
-                    
+                    page.fill(selector, "")
+
                     for char in text:
-                        # Playwright types with a small, realistic delay
-                        page.type(selector, char, delay=random.randint(5, 10)) 
-                        send_frontend_signal("type_char", selector, char) 
+                        page.type(selector, char, delay=random.randint(5, 10))
+                        yield from send_frontend_signal("type_char", selector, char)
                         time.sleep(0.01)
 
-                    send_frontend_signal("type_text", selector, "Typing Complete")
+                    yield from send_frontend_signal("type_text", selector, "Typing Complete")
                     time.sleep(1)
-                
+
                 elif action_type == "scroll":
-                    scroll_target = step.get("target", "bottom")
-                    send_frontend_signal("start_visual_automation", "body", f"Scrolling to {scroll_target}...")
-                    if scroll_target == "bottom":
+                    target = step.get("target", "bottom")
+                    yield from send_frontend_signal("start_visual_automation", "body", f"Scrolling to {target}...")
+
+                    if target == "bottom":
                         page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
-                    elif scroll_target == "top":
+                    elif target == "top":
                         page.evaluate("window.scrollTo(0, 0)")
-                    elif scroll_target.startswith("#") or scroll_target.startswith("."):
-                         # Scroll to a specific element
-                         page.locator(scroll_target).scroll_into_view_if_needed()
-                    send_frontend_signal("scroll", "body", scroll_target)
+                    else:
+                        page.locator(target).scroll_into_view_if_needed()
+
+                    yield from send_frontend_signal("scroll", "body", target)
                     time.sleep(1)
-                    
+
                 elif action_type == "wait":
                     wait_time = step.get("time", 1)
-                    send_frontend_signal("start_visual_automation", "body", f"Waiting for {wait_time} seconds...")
+                    yield from send_frontend_signal("start_visual_automation", "body", f"Waiting {wait_time}s...")
                     time.sleep(wait_time)
 
-
-            # --- Capture Proof ---
+            # === CAPTURE PROOF ===
             page.screenshot(path="/tmp/agent_proof.png")
-            final_content = page.locator("body").inner_text() 
-            final_proof = final_content[:1000]
-            
-            # --- Signal Completion ---
-            send_frontend_signal("end_visual_automation")
-            
-            return f"\n\n[AGENT PROOF] Action completed on {target_url}. Final page text snapshot:\n\n---\n{final_proof}\n---"
+            final_content = page.locator("body").inner_text()
+            proof = final_content[:1000]
+
+            yield from send_frontend_signal("end_visual_automation")
+
+            return f"\n\n[AGENT PROOF] Action completed on {target_url}.\n\n---\n{proof}\n---"
 
     except Exception as e:
         print(f"[AGENT] ❌ Playwright Error: {e}")
-        send_frontend_signal("end_visual_automation")
+        yield from send_frontend_signal("end_visual_automation")
         return f"\n\n[AGENT PROOF] Automation failed on {target_url}: {e}"
+
     finally:
         if browser:
             browser.close()
         print("[AGENT] 🛑 Playwright Session Closed.")
 # =========================
-# 🔥 DEEP RESEARCH MODE
-# =========================
-def deep_research_mode(user_query: str, history=None, num_sources=15):
-    """
-    Workflow singkat:
-    1) Fetch sources.
-    2) Buat draft + final answer.
-    """
-    wib = timezone(timedelta(hours=7))
-    now = datetime.now(wib)
-
-    # 1) Search
-    search_block = serpapi_search(user_query, num_results=num_sources)
-
-    # 2) System prompt + draft
-    system_prompt = """
-You are an expert researcher.
-Use the search results as evidence to create a comprehensive, well-structured, and referenced answer.
-Include summary, analysis, comparisons, and actionable recommendations.
-Write clearly and highlight key patterns or insights.
-"""
-    initial_prompt = f"""
-SYSTEM PROMPT:
-{system_prompt}
-
-Time: {now.strftime('%Y-%m-%d %H:%M:%S WIB')}
-
-USER QUESTION:
-{user_query}
-
-SEARCH RESULTS:
-{search_block}
-
-Produce a comprehensive draft answer (long form).
-"""
-    draft = call_chat_once(initial_prompt, history)
-    if not draft:
-        return "[DEEP-RESEARCH] ❌ Failed to create draft."
-
-    # 3) Final Answer Preparation
-    final_prompt = f"""
-FINAL ANSWER PREPARATION:
-
-USER QUESTION:
-{user_query}
-
-DRAFT:
-{draft}
-
-INSTRUCTIONS:
-- Provide a clear final answer.
-- Include 3-4 line TL;DR at the top.
-- List top 5 references (title + URL) from search results.
-- Add actionable recommendations.
-- End with 'END OF DEEP RESEARCH'.
-"""
-    final_answer = call_chat_once(final_prompt, history)
-    if not final_answer:
-        return "[DEEP-RESEARCH] ❌ Failed to produce final answer."
-
-    return final_answer
-# =========================
 # Chat Endpoint (Text + Voice)
 # =========================
 @app.route("/chat", methods=["POST"])