Update app.py

app.py

@@ -1,308 +1,48 @@
-import os
-import subprocess
-import random
-from huggingface_hub import InferenceClient
-import gradio as gr
-from safe_search import safe_search
-from i_search import google
-from i_search import i_search as i_s
-from datetime import datetime
-import logging
-import json
-
-# --- Configuration ---
-MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"  # Model to use
-MAX_HISTORY_TURNS = 5  # Number of history turns to keep
-VERBOSE = True  # Enable verbose logging
-
-# --- Logging Setup ---
-logging.basicConfig(
-    filename="app.log",  # Name of the log file
-    level=logging.INFO,  # Set the logging level (INFO, DEBUG, etc.)
-    format="%(asctime)s - %(levelname)s - %(message)s",
-)
-
-# --- Agent Definitions ---
-agents = {
-    "WEB_DEV": {
-        "description": "Specialized in web development tasks.",
-        "system_prompt": "You are a helpful AI assistant specializing in web development. You can generate code, answer questions, and provide guidance on web technologies.",
-    },
-    "AI_SYSTEM_PROMPT": {
-        "description": "Focuses on generating system prompts for AI agents.",
-        "system_prompt": "You are a helpful AI assistant that generates effective system prompts for AI agents. Your prompts should be clear, concise, and provide specific instructions.",
-    },
-    "PYTHON_CODE_DEV": {
-        "description": "Expert in Python code development.",
-        "system_prompt": "You are a helpful AI assistant specializing in Python code development. You can generate Python code, debug code, and answer questions about Python.",
-    },
-    "DATA_SCIENCE": {
-        "description": "Expert in data science tasks.",
-        "system_prompt": "You are a helpful AI assistant specializing in data science. You can analyze data, build models, and provide insights.",
-    },
-    "GAME_DEV": {
-        "description": "Expert in game development tasks.",
-        "system_prompt": "You are a helpful AI assistant specializing in game development. You can generate game logic, design levels, and provide guidance on game engines.",
-    },
-    # Add more agents as needed
-}
-
-# --- Function to format prompt with history ---
-def format_prompt(message, history, agent_name, system_prompt):
-    prompt = " "
-    for user_prompt, bot_response in history[-MAX_HISTORY_TURNS:]:
-        prompt += f"[INST] {user_prompt} [/  "
-        prompt += f" {bot_response}"
-    prompt += f"[INST] {message} [/  "
-
-    # Add system prompt if provided
-    if system_prompt:
-        prompt = f"{system_prompt}\n\n{prompt}"
-
-    return prompt
-
-# --- Function to run the LLM with specified parameters ---
-def run_llm(
-    prompt,
-    stop_sequences,
-    max_tokens,
-    temperature=0.7,
-    top_p=0.8,
-    repetition_penalty=1.5,
-):
-    seed = random.randint(1, 1111111111111111)
-    logging.info(f"Seed: {seed}")  # Log the seed
-
-    client = InferenceClient(MODEL_NAME)
-    resp = client.text_generation(
-        prompt,
-        max_new_tokens=max_tokens,
-        stop_sequences=stop_sequences,
-        temperature=temperature,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-    )
-    if VERBOSE:
-        logging.info(f"Prompt: {prompt}")
-        logging.info(f"Response: {resp}")
-    return resp
-
-# --- Function to handle agent interactions ---
-def agent_interaction(
-    purpose,
-    message,
-    agent_name,
-    system_prompt,
-    history,
-    temperature,
-    max_new_tokens,
-    top_p,
-    repetition_penalty,
-):
-    # Format the prompt with history
-    prompt = format_prompt(message, history, agent_name, system_prompt)
-
-    # Run the LLM
+===== Application Startup at 2024-07-13 21:48:51 =====
+
+Running on local URL:  http://0.0.0.0:7860
+
+To create a public link, set `share=True` in `launch()`.
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status
+    response.raise_for_status()
+  File "/usr/local/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status
+    raise HTTPError(http_error_msg, response=self)
+requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/site-packages/gradio/queueing.py", line 541, in process_events
+    response = await route_utils.call_process_api(
+  File "/usr/local/lib/python3.10/site-packages/gradio/route_utils.py", line 276, in call_process_api
+    output = await app.get_blocks().process_api(
+  File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1928, in process_api
+    result = await self.call_function(
+  File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1514, in call_function
+    prediction = await anyio.to_thread.run_sync(
+  File "/usr/local/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
+    return await get_async_backend().run_sync_in_worker_thread(
+  File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread
+    return await future
+  File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 859, in run
+    result = context.run(func, *args)
+  File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 833, in wrapper
+    response = f(*args, **kwargs)
+  File "/home/user/app/app.py", line 276, in chat
+    history, history_output = agent_interaction(
+  File "/home/user/app/app.py", line 106, in agent_interaction
     response = run_llm(
-        prompt,
-        stop_sequences=["observation:", "task:", "action:", "thought:"],
-        max_tokens=max_new_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-    )
-
-    # Update history
-    history.append((message, response))
-    return history, history
-
-# --- Function to parse actions from LLM response ---
-def parse_action(line):
-    """Parse the action line to get the action name and input."""
-    parts = line.split(":", 1)
-    if len(parts) == 2:
-        action_name = parts[0].replace("action", "").strip()
-        action_input = parts[1].strip()
-    else:
-        action_name = parts[0].replace("action", "").strip()
-        action_input = ""
-    return action_name, action_input
-
-# --- Function to execute actions based on agent's response ---
-def execute_action(purpose, task, history, action_name, action_input):
-    logging.info(f"Executing Action: {action_name} - {action_input}")
-
-    if action_name == "SEARCH":
-        try:
-            if "http" in action_input:
-                if "<" in action_input:
-                    action_input = action_input.strip("<")
-                if ">" in action_input:
-                    action_input = action_input.strip(">")
-                response = i_s(action_input)
-                logging.info(f"Search Result: {response}")
-                history += "observation: search result is: {}\n".format(response)
-            else:
-                history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=https://URL'\n"
-        except Exception as e:
-            history += "observation: {}\n".format(e)
-        return "MAIN", None, history, task
-
-    elif action_name == "COMPLETE":
-        task = "END"
-        return "COMPLETE", "COMPLETE", history, task
-
-    elif action_name == "GENERATE_CODE":
-        # Simulate OpenAI API response for code generation (using Hugging Face model)
-        # ... (Implement code generation logic using a suitable Hugging Face model)
-        # Example:
-        # code = generate_code_from_huggingface_model(action_input)  # Replace with actual code generation function
-        # history += f"observation: Here's the code: {code}\n"
-        # return "MAIN", None, history, task
-        pass  # Placeholder for code generation logic
-
-    elif action_name == "RUN_CODE":
-        # Simulate OpenAI API response for code execution (using Hugging Face model)
-        # ... (Implement code execution logic using a suitable Hugging Face model)
-        # Example:
-        # output = execute_code_from_huggingface_model(action_input)  # Replace with actual code execution function
-        # history += f"observation: Code output: {output}\n"
-        # return "MAIN", None, history, task
-        pass  # Placeholder for code execution logic
-
-    else:
-        # Default action: "MAIN"
-        return "MAIN", action_input, history, task
-
-# --- Function to handle the main loop of agent interaction ---
-def run_agent(purpose, history):
-    task = None
-    directory = "./"
-    if history:
-        history = str(history).strip("[]")
-    if not history:
-        history = ""
-
-    action_name = "UPDATE-TASK" if task is None else "MAIN"
-    action_input = None
-
-    while True:
-        logging.info(f"---")
-        logging.info(f"Purpose: {purpose}")
-        logging.info(f"Task: {task}")
-        logging.info(f"---")
-        logging.info(f"History: {history}")
-        logging.info(f"---")
-
-        # Get the agent's next action
-        prompt = f"""
-        You are a helpful AI assistant. You are working on the task: {task}
-        Your current history is:
-        {history}
-        What is your next thought?
-        thought: 
-        What is your next action?
-        action: 
-        """
-
-        response = run_llm(
-            prompt,
-            stop_sequences=["observation:", "task:", "action:", "thought:"],
-            max_tokens=32000,
-        )
-
-        # Parse the action
-        lines = response.strip().strip("\n").split("\n")
-        for line in lines:
-            if line.startswith("thought: "):
-                history += "{}\n".format(line)
-                logging.info(f"Thought: {line}")
-            elif line.startswith("action: "):
-                action_name, action_input = parse_action(line)
-                logging.info(f"Action: {action_name} - {action_input}")
-                history += "{}\n".format(line)
-                break
-
-        # Execute the action
-        action_name, action_input, history, task = execute_action(
-            purpose, task, history, action_name, action_input
-        )
-
-        yield (history)
-        if task == "END":
-            return (history)
-
-# --- Gradio Interface ---
-def main():
-    with gr.Blocks() as demo:
-        gr.Markdown("## FragMixt - No-Code Development Powerhouse")
-        gr.Markdown("### Your AI-Powered Development Companion")
-
-        # Chat Interface
-        chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel")
-
-        # Input Components
-        message = gr.Textbox(label="Enter your message", placeholder="Ask me anything!")
-        purpose = gr.Textbox(label="Purpose", placeholder="What is the purpose of this interaction?")
-        agent_name = gr.Dropdown(label="Agents", choices=list(agents.keys()), value=list(agents.keys())[0], interactive=True)
-        system_prompt = gr.Textbox(label="System Prompt", max_lines=1, interactive=True)
-        temperature = gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs")
-        max_new_tokens = gr.Slider(label="Max new tokens", value=1048 * 10, minimum=0, maximum=1048 * 10, step=64, interactive=True, info="The maximum numbers of new tokens")
-        top_p = gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens")
-        repetition_penalty = gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens")
-
-        # Button to submit the message
-        submit_button = gr.Button(value="Send")
-
-        # Project Explorer Tab (Placeholder)
-        with gr.Tab("Project Explorer"):
-            project_path = gr.Textbox(label="Project Path", placeholder="/home/user/app/current_project")
-            explore_button = gr.Button(value="Explore")
-            project_output = gr.Textbox(label="File Tree", lines=20)
-
-        # Chat App Logic Tab
-        with gr.Tab("Chat App"):
-            history = gr.State([])
-            examples = [
-                ["What is the purpose of this AI agent?", "I am designed to assist with no-code development tasks."],
-                ["Can you help me generate a Python function to calculate the factorial of a number?", "Sure! Here is a Python function to calculate the factorial of a number:"],
-            ]
-
-        def chat(purpose, message, agent_name, system_prompt, temperature, max_new_tokens, top_p, repetition_penalty, history):
-            # Get the system prompt for the selected agent
-            system_prompt = agents.get(agent_name, {}).get("system_prompt", "")
-
-            # Run the agent interaction
-            history, history_output = agent_interaction(
-                purpose,
-                message,
-                agent_name,
-                system_prompt,
-                history,
-                temperature,
-                max_new_tokens,
-                top_p,
-                repetition_penalty,
-            )
-            return history, history_output
-
-        submit_button.click(
-            chat,
-            inputs=[
-                purpose,
-                message,
-                agent_name,
-                system_prompt,
-                temperature,
-                max_new_tokens,
-                top_p,
-                repetition_penalty,
-                history,
-            ],
-            outputs=[chatbot, history],
-        )
-
-    demo.launch()
-
-if __name__ == "__main__":
-    main()
+  File "/home/user/app/app.py", line 77, in run_llm
+    resp = client.text_generation(
+  File "/usr/local/lib/python3.10/site-packages/huggingface_hub/inference/_client.py", line 2061, in text_generation
+    raise_text_generation_error(e)
+  File "/usr/local/lib/python3.10/site-packages/huggingface_hub/inference/_common.py", line 460, in raise_text_generation_error
+    raise http_error
+  File "/usr/local/lib/python3.10/site-packages/huggingface_hub/inference/_client.py", line 2032, in text_generation
+    bytes_output = self.post(json=payload, model=model, task="text-generation", stream=stream)  # type: ignore
+  File "/usr/local/lib/python3.10/site-packages/huggingface_hub/inference/_client.py", line 273, in post
+    hf_raise_for_status(response)
+  File "/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 371, in hf_raise_for_status
+    raise HfHubHTTPError(str(e), response=response) from e
+huggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1 (Request ID: 41TZN5nlKL2We1-SnVOGU)