Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import os
 import time
-from cerebras.cloud.sdk import Cerebras
 import requests
 from bs4 import BeautifulSoup
 from urllib.parse import urljoin, urlparse
@@ -30,9 +29,8 @@ gemma2-9b-it	30	14,400	15,000	500,000
 llama-3.1-70b-versatile	30	14,400	6,000	200,000
 llama-3.1-8b-instant	30	14,400	20,000	500,000
 llama-3.2-11b-text-preview	30	7,000	7,000	500,000
-llama-3.2-11b-vision-preview	30	7,000	7,000	500,000
-llama-3.2-1b-preview	30	7,000	7,000	500,000
-llama-3.2-3b-preview	30	7,000	7,000	500,000
+llama-3.2-11b-vision-preview)	30	7,000	7,000	500,000
+llama-3.2-1b-preview)	30	7,000	7,000	500,000
 llama-3.2-90b-text-preview	30	7,000	7,000	500,000
 llama-3.2-90b-vision-preview	15	3,500	7,000	250,000
 llama-3.3-70b-specdec	30	1,000	6,000	100,000
@@ -46,24 +44,20 @@ llava-v1.5-7b-4096-preview	30	14,400	30,000	(No limit)
 mixtral-8x7b-32768	30	14,400	5,000	500,000
 """
 
-SPEECH_TO_TEXT_MODELS_INFO = """
-Speech To Text
+SPEECH ToText
 ID	Requests per Minute	Requests per Day	Audio Seconds per Hour	Audio Seconds per Day
 distil-whisper-large-v3-en	20	2,000	7,200	28,800
 whisper-large-v3	20	2,000	7,200	28,800
 whisper-large-v3-turbo	20	2,000	7,200	28,800
-"""
 
 def get_model_info():
     return f"""
-        {CHAT_COMPLETION_MODELS_INFO}
+        {CHAT_COMPLETION_MODELS_INFO)
 
         {SPEECH_TO_TEXT_MODELS_INFO}
     """
 
-
 # --- Helper Functions ---
-
 def is_valid_url(url):
     try:
         result = urlparse(url)
@@ -71,37 +65,35 @@ def is_valid_url(url):
     except ValueError:
         return False
 
-
 def fetch_webpage(url):
     try:
         response = requests.get(url, timeout=10)
-        response.raise_for_status()  # Raise an exception for bad status codes
+        response.raise_for_status()
         return response.text
     except requests.exceptions.RequestException as e:
         return f"Error fetching URL: {e}"
 
-
 def extract_text_from_html(html):
-    soup = BeautifulSoup(html, 'html.parser')
+    soup = BeautifulSoup(html, 'html.parser)
     text = soup.get_text(separator=' ', strip=True)
     return text
 
-
 # --- Chat Logic with Groq ---
 async def chat_with_groq(user_input, chat_history):
     start_time = time.time()
     try:
-        # Prepare chat history for the prompt
         formatted_history = "\n".join([f"User: {msg[0]}\nAI: {msg[1]}" for msg in chat_history[-10:]])
 
         messages = [
-            {"role": "system", "content": f"""You are IntellijMind, a highly advanced and proactive AI agent. You are designed to assist users in achieving their goals through detailed insights, creative problem-solving, and the use of various tools.  Your objective is to understand the user's intentions, break them into logical steps, and use available tools when needed to achieve the best outcome. Available tools: scrape with a URL, and search_internet with a query. Be creative and inject humor when appropriate. You have access to multiple tools to help the user with their requests. Available actions: take_action: 'scrape', parameters: url, take_action: 'search_internet', parameters: query. Example action: Action: take_action, Parameters: {{"action":"scrape", "url":"https://example.com"}} or  Action: take_action, Parameters: {{"action":"search_internet", "query":"latest news on AI"}} . Current conversation: {formatted_history}"""},
+            {"role": "system", "content": f"""
+                You are IntellijMind, a highly advanced and proactive AI agent. You are designed to assist users in achieving their goals through detailed insights, creative problem-solving, and the use of various tools. Your objective is to understand the user's intentions, break them into logical steps, and use available tools when needed to achieve the best outcome. Available tools: scrape with a URL, and search_internet with a query. Be creative and inject humor when appropriate. You have access to multiple tools to help the user with their requests. Available actions: take_action: 'scrape', parameters: url, take_action: 'search_internet', parameters: query. Example action: Action: take_action, Parameters: {{"action":"scrape", "url":"https://example.com"}} or  Action: take_action, Parameters: {{"action":"search_internet", "query":"latest news on AI"}} . Current conversation: {formatted_history}
+            }},
             {"role": "user", "content": user_input}
         ]
 
         if user_input.lower() == "model info":
-             response = get_model_info()
-             return response, "", f"Compute Time: {time.time() - start_time:.2f} seconds", f"Tokens used: {len(user_input.split()) + len(response.split())}"
+            response = get_model_info()
+            return response, "", f"Compute Time: {time.time() - start_time:.2f} seconds", f"Tokens used: {len(user_input.split()) + len(response.split())}"
 
         completion = client_groq.chat.completions.create(
             model="llama3-groq-70b-8192-tool-use-preview",
@@ -126,7 +118,7 @@ async def chat_with_groq(user_input, chat_history):
                 if "Action:" in content:
                     action_match = re.search(r"Action: (\w+), Parameters: (\{.*\})", content)
                     if action_match and tool_execution_count < 3:  # Limit tool use to avoid infinite loops
-                        tool_execution_count +=1
+                        tool_execution_count += 1
                         action = action_match.group(1)
                         parameters = json.loads(action_match.group(2))
                         if action == "take_action":
@@ -136,7 +128,7 @@ async def chat_with_groq(user_input, chat_history):
                                     html_content = fetch_webpage(url)
                                     if not html_content.startswith("Error"):
                                         webpage_text = extract_text_from_html(html_content)
-                                        response += f"\nWebpage Content: {webpage_text}\n"
+                                        response += f"\nWebpage Content: {webpage_text}\n")
                                     else:
                                        response += f"\nError scraping webpage: {html_content}\n"
                                 else:
@@ -144,8 +136,7 @@ async def chat_with_groq(user_input, chat_history):
                             elif parameters.get("action") == "search_internet":
                                 query = parameters.get("query")
                                 response += f"\n Search query: {query}. Note: Search is simulated in this environment. Results may vary. \n"
-                                # Replace the line with a real internet search if you have a search api
-                                response += f"\n Search Results: Mock Results for query: {query} \n"
+                                response += f"\nSearch Results: Mock Results for query: {query} \n"
 
         compute_time = time.time() - start_time
         token_usage = len(user_input.split()) + len(response.split())
@@ -154,7 +145,6 @@ async def chat_with_groq(user_input, chat_history):
     except Exception as e:
         return "Error: Unable to process your request.", "", str(e), ""
 
-
 # --- Gradio Interface ---
 def gradio_ui():
     with gr.Blocks() as demo:
@@ -170,7 +160,6 @@ def gradio_ui():
 
         user_input = gr.Textbox(label="Type your message", placeholder="Ask me anything...", lines=2)
 
-
         with gr.Row():
             send_button = gr.Button("Send", variant="primary")
             clear_button = gr.Button("Clear Chat")
@@ -178,11 +167,10 @@ def gradio_ui():
 
         async def handle_chat(chat_history, user_input):
             if not user_input.strip():
-                return chat_history, "", "", "", "Please enter a valid message."
+                return chat_history, "", "", "", "Please enter a valid message.")
 
             ai_response, chain_of_thought, compute_info, token_usage = await chat_with_groq(user_input, chat_history)
 
-
             chat_history.append((user_input, ai_response))
             return chat_history, chain_of_thought, compute_info, token_usage
 
@@ -199,12 +187,12 @@ def gradio_ui():
             return f"Chat history exported to {filename}.", ""
 
         send_button.click(handle_chat, inputs=[chat_history, user_input], outputs=[chat_history, chain_of_thought_display, compute_time, token_usage_display])
-        clear_button.click(clear_chat, outputs=[chat_history, chain_of_thought_display, compute_time, token_usage_display])
-        export_button.click(export_chat, inputs=[chat_history], outputs=[compute_time, chain_of_thought_display])
+        clear_button.click(clear_chat, outputs=[chat_history, chain_of(thought_display, compute_time, token_usage_display])
+        export_button.click(export_chat, inputs=[chat_history], outputs=[compute_time, chain_of(thought_display])
 
-        user_input.submit(handle_chat, inputs=[chat_history, user_input], outputs=[chat_history, chain_of_thought_display, compute_time, token_usage_display])
+        user_input.submit(handle_chat, inputs=[chat_history, user_input], outputs=[chat_history, chain_of(thought_display, compute_time, token_usage_display])
 
-        gr.Markdown("""---\n### 🌟 Features:\n- **Autonomous Agent**: Proactively pursues your goals.\n- **Advanced Tool Use**: Utilizes multiple tools like web scraping and search.\n- **Dynamic and Creative**: Engages with humor and creative responses.\n- **Enhanced Chat History**: Maintains better context of the conversation.\n- **Real-Time Performance Metrics**: Measure response compute time instantly.\n- **Token Usage Tracking**: Monitor token usage per response for transparency.\n- **Export Chat History**: Save your conversation as a text file for future reference.\n- **User-Friendly Design**: Intuitive chatbot interface with powerful features.\n- **Insightful Chain of Thought**: See the reasoning process behind AI decisions.\n- **Submit on Enter**: Seamless interaction with keyboard support.\n""")
+        gr.Markdown("""---\n### 🌟 Features:\n- **Autonomous Agent**: Proactively pursues your goals.\n- **Advanced Tool Use**: Utilizes multiple tools like web scraping and search.\n- **Dynamic and Creative**: Engages with humor and creative responses.\n- **Enhanced Chat History**: Maintains better context of the conversation.\n- **Real-Time Performance Metrics**: Measure response compute time instantly.\n- **Token Usage Tracking**: Monitor token usage per response for transparency.\n- **Export Chat History**: Save your conversation(as a text(file(for future(reference.\n- **User-Friendly Design**: Intuitive chatbot(interface(with(powerful(features.\n- **Insightful Chain of Thought**: See the reasoning process behind AI decisions.\n- **Submit on Enter**: Seamless interaction with keyboard support.\n""")
 
     return demo