Update app.py

app.py

@@ -1,48 +1,308 @@
-===== 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(
-  File "/home/user/app/app.py", line 77, in run_llm
+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(
-  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)
+        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
+    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()