Update app.py

app.py

@@ -1,69 +1,21 @@
 import gradio as gr
 import os
 import time
-from cerebras.cloud.sdk import Cerebras
+from groq import Groq
 import requests
 from bs4 import BeautifulSoup
 from urllib.parse import urljoin, urlparse
-from groq import Groq
 import re
 import json
 
-# --- Constants and API Setup ---
-CEREBRAS_API_KEY = os.getenv("CEREBRAS_API_KEY")
-if not CEREBRAS_API_KEY:
-    raise ValueError("CEREBRAS_API_KEY environment variable is not set.")
-
+# API Setup
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 if not GROQ_API_KEY:
     raise ValueError("GROQ_API_KEY environment variable is not set.")
 
-client_cerebras = Cerebras(api_key=CEREBRAS_API_KEY)
-client_groq = Groq(api_key=GROQ_API_KEY)
-
-# --- Model Rate Limit Info ---
-CHAT_COMPLETION_MODELS_INFO = """
-Chat Completion
-ID	Requests per Minute	Requests per Day	Tokens per Minute	Tokens per Day
-gemma-7b-it	30	14,400	15,000	500,000
-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-3.2-90b-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
-llama-3.3-70b-versatile	30	1,000	6,000	100,000
-llama-guard-3-8b	30	14,400	15,000	500,000
-llama3-70b-8192	30	14,400	6,000	500,000
-llama3-8b-8192	30	14,400	30,000	500,000
-llama3-groq-70b-8192-tool-use-preview	30	14,400	15,000	500,000
-llama3-groq-8b-8192-tool-use-preview	30	14,400	15,000	500,000
-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
-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}
-
-        {SPEECH_TO_TEXT_MODELS_INFO}
-    """
-
-
-# --- Helper Functions ---
+client = Groq(api_key=GROQ_API_KEY)
 
+# Helper Functions
 def is_valid_url(url):
     try:
         result = urlparse(url)
@@ -71,64 +23,72 @@ 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')
     text = soup.get_text(separator=' ', strip=True)
     return text
 
-
-# --- Chat Logic with Groq ---
-async def chat_with_groq(user_input, chat_history):
+# Chat Logic
+async def chat_with_agent(user_input, chat_history):
     start_time = time.time()
     try:
-        # Prepare chat history for the prompt
+        # Prepare chat history
         formatted_history = "\n".join([f"User: {msg[0]}\nAI: {msg[1]}" for msg in chat_history[-10:]])
 
+        system_prompt = """You are TaskMaster, an advanced agentic AI designed to help users accomplish their goals through:
+        1. Understanding and breaking down complex tasks
+        2. Using available tools effectively
+        3. Providing creative solutions with occasional humor
+        4. Maintaining context and adapting to user needs
+        
+        Available tools:
+        - Web scraping (URL required)
+        - Internet search simulation
+        
+        You can take actions using:
+        Action: take_action, Parameters: {"action":"scrape", "url":"https://example.com"}
+        Action: take_action, Parameters: {"action":"search_internet", "query":"search query"}"""
+
         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": system_prompt},
             {"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())}"
-
-        completion = client_groq.chat.completions.create(
-            model="llama3-groq-70b-8192-tool-use-preview",
+        completion = client.chat.completions.create(
             messages=messages,
-            temperature=1,
+            temperature=0.7,
             max_tokens=2048,
-            top_p=1,
             stream=True,
-            stop=None,
+            stop=None
         )
 
         response = ""
         chain_of_thought = ""
         tool_execution_count = 0
+        
         for chunk in completion:
             if chunk.choices[0].delta and chunk.choices[0].delta.content:
                 content = chunk.choices[0].delta.content
                 response += content
+                
                 if "Chain of Thought:" in content:
                     chain_of_thought += content.split("Chain of Thought:", 1)[-1]
 
-                if "Action:" in content:
+                if "Action:" in content and tool_execution_count < 3:
                     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
+                    if action_match:
+                        tool_execution_count += 1
                         action = action_match.group(1)
                         parameters = json.loads(action_match.group(2))
+                        
                         if action == "take_action":
                             if parameters.get("action") == "scrape":
                                 url = parameters.get("url")
@@ -136,53 +96,52 @@ 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[:1000]}...\n"
                                     else:
-                                       response += f"\nError scraping webpage: {html_content}\n"
+                                        response += f"\nError scraping webpage: {html_content}\n"
                                 else:
-                                   response +=  "\nInvalid URL provided.\n"
+                                    response += "\nInvalid URL provided.\n"
                             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"\nSearching for: {query}\nSimulated search results would appear here.\n"
 
         compute_time = time.time() - start_time
         token_usage = len(user_input.split()) + len(response.split())
         return response, chain_of_thought, f"Compute Time: {compute_time:.2f} seconds", f"Tokens used: {token_usage}"
 
     except Exception as e:
-        return "Error: Unable to process your request.", "", str(e), ""
+        return f"Error: {str(e)}", "", "Error occurred", ""
 
-
-# --- Gradio Interface ---
-def gradio_ui():
-    with gr.Blocks() as demo:
-        gr.Markdown("""# 🚀 IntellijMind: The Autonomous AI Agent\nExperience the forefront of AI capabilities, where the agent proactively achieves your goals!""")
+# Gradio Interface
+def create_interface():
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("""# 🤖 TaskMaster: Your AI Assistant
+        Let me help you accomplish your goals through intelligent task execution!""")
 
         with gr.Row():
             with gr.Column(scale=6):
-                chat_history = gr.Chatbot(label="Chat History")
+                chat_history = gr.Chatbot(label="Chat History", height=600)
             with gr.Column(scale=2):
-                compute_time = gr.Textbox(label="Compute Time", interactive=False)
-                chain_of_thought_display = gr.Textbox(label="Chain of Thought", interactive=False, lines=10)
-                token_usage_display = gr.Textbox(label="Token Usage", interactive=False)
-
-        user_input = gr.Textbox(label="Type your message", placeholder="Ask me anything...", lines=2)
-
+                compute_time = gr.Textbox(label="Performance Metrics", interactive=False)
+                chain_of_thought_display = gr.Textbox(label="Reasoning Process", interactive=False, lines=10)
+                token_usage_display = gr.Textbox(label="Resource Usage", interactive=False)
+
+        user_input = gr.Textbox(
+            label="Your Request",
+            placeholder="What would you like me to help you with?",
+            lines=2
+        )
 
         with gr.Row():
             send_button = gr.Button("Send", variant="primary")
-            clear_button = gr.Button("Clear Chat")
-            export_button = gr.Button("Export Chat History")
+            clear_button = gr.Button("Clear")
+            export_button = gr.Button("Save Chat")
 
         async def handle_chat(chat_history, user_input):
             if not user_input.strip():
-                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)
-
-
+                return chat_history, "", "", ""
+            
+            ai_response, chain_of_thought, compute_info, token_usage = await chat_with_agent(user_input, chat_history)
             chat_history.append((user_input, ai_response))
             return chat_history, chain_of_thought, compute_info, token_usage
 
@@ -191,23 +150,33 @@ def gradio_ui():
 
         def export_chat(chat_history):
             if not chat_history:
-                return "", "No chat history to export."
-            chat_text = "\n".join([f"User: {item[0]}\nAI: {item[1]}" for item in chat_history])
-            filename = f"chat_history_{int(time.time())}.txt"
+                return "No chat history to export.", ""
+            
+            filename = f"taskmaster_chat_{int(time.time())}.txt"
+            chat_text = "\n".join([f"User: {item[0]}\nAI: {item[1]}\n" for item in chat_history])
+            
             with open(filename, "w") as file:
                 file.write(chat_text)
-            return f"Chat history exported to {filename}.", ""
+            return f"Chat saved to {filename}", ""
 
+        # Event handlers
         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])
-
         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("""### 🌟 Capabilities:
+        - Task Analysis & Breakdown
+        - Web Information Retrieval
+        - Creative Problem-Solving
+        - Context-Aware Responses
+        - Performance Tracking
+        - Chat Export
+        """)
 
     return demo
 
-# Run the Gradio app
-demo = gradio_ui()
-demo.launch()
+# Launch the application
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()