Update app.py
Browse files
app.py
CHANGED
@@ -1,72 +1,12 @@
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import socket
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import subprocess
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import gradio as gr
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from openai import OpenAI
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import json
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import sys
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from io import StringIO
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import traceback
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import matplotlib
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matplotlib.use("Agg") # Use non-interactive backend
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import matplotlib.pyplot as plt
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import base64
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from io import BytesIO
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subprocess.Popen("bash /home/user/app/start.sh", shell=True)
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client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="sk-local", timeout=600)
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def execute_python_code(code):
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"""Execute Python code safely and return results"""
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# Capture stdout
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old_stdout = sys.stdout
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sys.stdout = StringIO()
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# Store any plots
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plt.clf() # Clear any existing plots
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try:
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# Execute the code
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exec_globals = {
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"plt": plt,
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"matplotlib": matplotlib,
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"__builtins__": __builtins__,
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# Add other safe modules as needed
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"json": json,
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"math": __import__("math"),
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"numpy": __import__("numpy"), # if available
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"pandas": __import__("pandas"), # if available
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}
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exec(code, exec_globals)
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# Get printed output
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output = sys.stdout.getvalue()
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# Check if there are any plots
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plot_data = None
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if plt.get_fignums(): # If there are active figures
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buf = BytesIO()
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plt.savefig(buf, format="png", bbox_inches="tight", dpi=150)
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buf.seek(0)
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plot_data = base64.b64encode(buf.read()).decode()
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plt.close("all") # Close all figures
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sys.stdout = old_stdout
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result = {"success": True, "output": output, "plot": plot_data}
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return result
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except Exception as e:
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sys.stdout = old_stdout
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error_msg = f"Error: {str(e)}\n{traceback.format_exc()}"
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return {"success": False, "output": error_msg, "plot": None}
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def handle_function_call(function_name, arguments):
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"""Handle function calls from the model"""
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if function_name == "browser_search":
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@@ -76,21 +16,12 @@ def handle_function_call(function_name, arguments):
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return f"Search results for '{query}' (max {max_results} results): [Implementation needed]"
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elif function_name == "code_interpreter":
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code = arguments.get("code", "")
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if not code:
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return "No code provided to execute."
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if result["success"]:
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response = f"Code executed successfully:\n\n```\n{result['output']}\n```"
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if result["plot"]:
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response += (
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f"\n\n[Plot generated - base64 data: {result['plot'][:50]}...]"
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)
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return response
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else:
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return f"Code execution failed:\n\n```\n{result['output']}\n```"
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return f"Unknown function: {function_name}"
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for chunk in stream:
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delta = chunk.choices[0].delta
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print('delta', delta)
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# Handle function calls
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if hasattr(delta, "tool_calls") and delta.tool_calls:
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}
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)
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output += delta.content
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except:
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if delta.content:
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output += delta.content
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yield output
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import subprocess
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import gradio as gr
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from openai import OpenAI
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import json
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subprocess.Popen("bash /home/user/app/start.sh", shell=True)
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client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="sk-local", timeout=600)
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def handle_function_call(function_name, arguments):
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"""Handle function calls from the model"""
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if function_name == "browser_search":
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return f"Search results for '{query}' (max {max_results} results): [Implementation needed]"
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elif function_name == "code_interpreter":
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# Implement your code interpreter logic here
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code = arguments.get("code", "")
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if not code:
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return "No code provided to execute."
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return f"Code interpreter results for '{code}': [Implementation needed]"
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return f"Unknown function: {function_name}"
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for chunk in stream:
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delta = chunk.choices[0].delta
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# Handle function calls
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if hasattr(delta, "tool_calls") and delta.tool_calls:
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}
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)
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if hasattr(delta, "reasoning_content") and delta.reasoning_content:
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# output += delta.reasoning_content
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output = f"""*{output}{delta.reasoning_content}*\n"""
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elif delta.content:
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output += delta.content
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yield output
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