Update app.py

app.py

@@ -712,51 +712,16 @@ 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}"
 
-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, history, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2):
-    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_manager.get_conversation_list()
-    
-    conversation.add_message("user", message)
-    
-    chat_history = "\n".join([f"{msg['role']}: {msg['content']}" for msg in conversation.messages])
+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])
     
     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,
@@ -773,72 +738,44 @@ def chat_function(message, history, conversation_id, num_results, max_chars, tim
             use_pydf2=use_pydf2
         )
     
-    conversation.add_message("assistant", response)
-    history.append((message, response))
-    return history, conversation_id, conversation_manager.get_conversation_list()
-
-def load_conversation(evt: gr.SelectData):
-    conversation_id = evt.value['id']
-    conversation = conversation_manager.get_conversation(conversation_id)
-    if not conversation:
-        return [], ""
-    history = [(msg["content"], msg["content"]) if msg["role"] == "user" else (None, msg["content"]) for msg in conversation.messages]
-    return history, conversation.title, conversation_id
-
-# 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.Dataframe(
-                headers=["id", "title"],
-                datatype=["str", "str"],
-                label="Conversations",
-                interactive=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(
+    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(
             ["google", "bing", "duckduckgo", "baidu", "yahoo", "qwant", "startpage"],
             multiselect=True,
             value=["google", "duckduckgo", "bing", "qwant"],
             label="Engines"
-        )
-        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 bot(user_message, history, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2):
-        return chat_function(
-            user_message, history, conversation_id, num_results, max_chars, time_range,
-            language, category, engines, safesearch, method, llm_temperature, model, use_pydf2
-        )
-
-    send.click(
-        bot,
-        [msg, chatbot, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2],
-        [chatbot, conversation_id, conversation_list]
-    )
-
-    msg.submit(
-        bot,
-        [msg, chatbot, conversation_id, num_results, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2],
-        [chatbot, conversation_id, conversation_list]
+        ),
+        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,
     )
+)
 
-    conversation_list.select(load_conversation, None, [chatbot, msg, conversation_id])
-
-iface.launch(share=True)
+if __name__ == "__main__":
+    logger.info("Starting the SearXNG Scraper for Financial News using ChatInterface with Advanced Parameters")
+    iface.launch(share=True)