Update app.py

app.py

@@ -712,16 +712,51 @@ def search_and_scrape(query, chat_history, num_results=5, max_chars=3000, time_r
         logger.error(f"Unexpected error in search_and_scrape: {e}")
         return f"An unexpected error occurred during the search and scrape process: {e}"
 
-def chat_function(message: str, history: List[Tuple[str, str]], num_results: int, max_chars: int, time_range: str, language: str, category: str, engines: List[str], safesearch: int, method: str, llm_temperature: float, model: str, use_pydf2: bool):
-    chat_history = "\n".join([f"{role}: {msg}" for role, msg in history])
+class Conversation:
+    def __init__(self, id, title):
+        self.id = id
+        self.title = title
+        self.messages = []
+
+    def add_message(self, role, content):
+        self.messages.append({"role": role, "content": content})
+
+class ConversationManager:
+    def __init__(self):
+        self.conversations = {}
+        self.current_conversation_id = None
+
+    def create_conversation(self, title):
+        conversation_id = str(random.randint(1000, 9999))
+        self.conversations[conversation_id] = Conversation(conversation_id, title)
+        self.current_conversation_id = conversation_id
+        return conversation_id
+
+    def get_conversation(self, conversation_id):
+        return self.conversations.get(conversation_id)
+
+    def get_conversation_list(self):
+        return [{"id": conv.id, "title": conv.title} for conv in self.conversations.values()]
+
+conversation_manager = ConversationManager()
+
+def chat_function(message: str, history: List[Tuple[str, str]], conversation_id: str, num_results: int, max_chars: int, time_range: str, language: str, category: str, engines: List[str], safesearch: int, method: str, llm_temperature: float, model: str, use_pydf2: bool):
+    if not conversation_id:
+        conversation_id = conversation_manager.create_conversation(message[:30] + "...")
+    
+    conversation = conversation_manager.get_conversation(conversation_id)
+    if not conversation:
+        return "Error: Conversation not found", conversation_id
+    
+    conversation.add_message("user", message)
+    
+    chat_history = "\n".join([f"{msg['role']}: {msg['content']}" for msg in conversation.messages])
     
     query_type = determine_query_type(message, chat_history, client)
     
     if query_type == "knowledge_base":
         response = generate_ai_response(message, chat_history, client, model)
     else:  # web_search
-        gr.Info("Initiating Web Search")
-        yield "Request you to sit back and relax until I scrape the web for up-to-date information"
         response = search_and_scrape(
             query=message,
             chat_history=chat_history,
@@ -738,44 +773,74 @@ def chat_function(message: str, history: List[Tuple[str, str]], num_results: int
             use_pydf2=use_pydf2
         )
     
-    yield response
-
-
-iface = gr.ChatInterface(
-    chat_function,
-    title="Web Scraper for Financial News with Sentinel AI",
-    description="Ask Sentinel any question. It will search the web for recent information or use its knowledge base as appropriate.",
-    theme=gr.Theme.from_hub("allenai/gradio-theme"),
-    additional_inputs=[
-        gr.Slider(5, 20, value=10, step=1, label="Number of initial results"),
-        gr.Slider(500, 10000, value=1500, step=100, label="Max characters to retrieve"),
-        gr.Dropdown(["", "day", "week", "month", "year"], value="", label="Time Range"),
-        gr.Dropdown(["", "all", "en", "fr", "de", "es", "it", "nl", "pt", "pl", "ru", "zh"], value="", label="Language"),
-        gr.Dropdown(["", "general", "news", "images", "videos", "music", "files", "it", "science", "social media"], value="", label="Category"),
-        gr.Dropdown(
+    conversation.add_message("assistant", response)
+    return response, conversation_id
+
+def load_conversation(conversation_id: str):
+    conversation = conversation_manager.get_conversation(conversation_id)
+    if not conversation:
+        return [], ""
+    return conversation.messages, conversation.title
+
+# Create the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as iface:
+    conversation_id = gr.State("")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            conversation_list = gr.List(
+                conversation_manager.get_conversation_list(),
+                label="Conversations",
+                render=False
+            )
+        
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(height=500)
+            msg = gr.Textbox(label="Type your message here...")
+            send = gr.Button("Send")
+    
+    with gr.Accordion("Advanced Parameters", open=False):
+        num_results = gr.Slider(5, 20, value=10, step=1, label="Number of initial results")
+        max_chars = gr.Slider(500, 10000, value=1500, step=100, label="Max characters to retrieve")
+        time_range = gr.Dropdown(["", "day", "week", "month", "year"], value="", label="Time Range")
+        language = gr.Dropdown(["", "all", "en", "fr", "de", "es", "it", "nl", "pt", "pl", "ru", "zh"], value="", label="Language")
+        category = gr.Dropdown(["", "general", "news", "images", "videos", "music", "files", "it", "science", "social media"], value="", label="Category")
+        engines = gr.Dropdown(
             ["google", "bing", "duckduckgo", "baidu", "yahoo", "qwant", "startpage"],
             multiselect=True,
             value=["google", "duckduckgo", "bing", "qwant"],
             label="Engines"
-        ),
-        gr.Slider(0, 2, value=2, step=1, label="Safe Search Level"),
-        gr.Radio(["GET", "POST"], value="POST", label="HTTP Method"),
-        gr.Slider(0, 1, value=0.2, step=0.1, label="LLM Temperature"),
-        gr.Dropdown(["huggingface", "groq", "mistral"], value="mistral", label="LLM Model"),
-        gr.Checkbox(label="Use PyPDF2 for PDF scraping", value=False),
-    ],
-    additional_inputs_accordion=gr.Accordion("⚙️ Advanced Parameters", open=True),
-    retry_btn="Retry",
-    undo_btn="Undo",
-    clear_btn="Clear",
-    chatbot=gr.Chatbot(
-        show_copy_button=True,
-        likeable=True,
-        layout="bubble",
-        height=500,
+        )
+        safesearch = gr.Slider(0, 2, value=2, step=1, label="Safe Search Level")
+        method = gr.Radio(["GET", "POST"], value="POST", label="HTTP Method")
+        llm_temperature = gr.Slider(0, 1, value=0.2, step=0.1, label="LLM Temperature")
+        model = gr.Dropdown(["huggingface", "groq", "mistral"], value="mistral", label="LLM Model")
+        use_pydf2 = gr.Checkbox(label="Use PyPDF2 for PDF scraping", value=False)
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2):
+        user_message = history[-1][0]
+        bot_message, new_conversation_id = chat_function(
+            user_message, history, conversation_id, num_results, max_chars, time_range,
+            language, category, engines, safesearch, method, llm_temperature, model, use_pydf2
+        )
+        history[-1][1] = bot_message
+        return history, new_conversation_id, conversation_manager.get_conversation_list()
+
+    send.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot,
+        [chatbot, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2],
+        [chatbot, conversation_id, conversation_list]
     )
-)
 
-if __name__ == "__main__":
-    logger.info("Starting the SearXNG Scraper for Financial News using ChatInterface with Advanced Parameters")
-    iface.launch(share=True)
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot,
+        [chatbot, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2],
+        [chatbot, conversation_id, conversation_list]
+    )
+
+    conversation_list.select(load_conversation, [conversation_list], [chatbot, conversation_id])
+
+iface.launch(share=True)