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import streamlit as st
from interpreter import interpreter
import os
# Page configuration
st.set_page_config(page_title="AutoInterpreter", layout="wide")
# Initialize session state for settings if not exists
if "settings" not in st.session_state:
st.session_state.settings = {
"api_key": os.getenv("HF_API_KEY", ""),
"api_base": "/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2FQwen%2FQwen2.5-72B-Instruct%26quot%3B%3C%2Fspan%3E%2C
"model": "huggingface/Qwen/Qwen2.5-72B-Instruct",
"auto_run": True,
"context_window": 8000,
"max_tokens": 4000
}
# Create header with title and settings button
col1, col2 = st.columns([0.9, 0.1])
with col1:
st.markdown("# Autointerpreter")
st.markdown("Run Any Code. The Final AI Coding Experience.")
with col2:
settings_button = st.button("⚙️", help="Settings")
# Settings modal
if settings_button:
settings_modal = st.container()
with settings_modal:
st.markdown("### Settings")
cols = st.columns(2)
with cols[0]:
# API Settings
st.text_input(
"API Key",
value=st.session_state.settings["api_key"],
type="password",
key="api_key",
on_change=lambda: st.session_state.settings.update({"api_key": st.session_state.api_key})
)
st.text_input(
"Model",
value=st.session_state.settings["model"],
key="model",
on_change=lambda: st.session_state.settings.update({"model": st.session_state.model})
)
with cols[1]:
# Model Settings
st.toggle(
"Auto Run",
value=st.session_state.settings["auto_run"],
key="auto_run",
on_change=lambda: st.session_state.settings.update({"auto_run": st.session_state.auto_run})
)
st.number_input(
"Max Tokens",
value=st.session_state.settings["max_tokens"],
min_value=100,
max_value=8000,
key="max_tokens",
on_change=lambda: st.session_state.settings.update({"max_tokens": st.session_state.max_tokens})
)
# Apply settings to interpreter
interpreter.llm.api_key = st.session_state.settings["api_key"]
interpreter.llm.api_base = st.session_state.settings["api_base"]
interpreter.llm.model = st.session_state.settings["model"]
interpreter.auto_run = st.session_state.settings["auto_run"]
interpreter.context_window = st.session_state.settings["context_window"]
interpreter.max_tokens = st.session_state.settings["max_tokens"]
# Initialize messages session state
if "messages" not in st.session_state:
st.session_state.messages = []
# Clear button
if st.button("🗑️ Clear", help="Clear chat"):
interpreter.messages = []
st.session_state.messages = []
st.rerun()
# Display chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# User input
user_input = st.chat_input("Enter your message:")
if user_input:
# Display user message
st.chat_message("user").write(user_input)
st.session_state.messages.append({"role": "user", "content": user_input})
try:
# Create a chat message container for the assistant
with st.chat_message("assistant"):
response_placeholder = st.empty()
message_buffer = []
code_buffer = []
# Stream the response
for chunk in interpreter.chat(user_input, stream=True):
if isinstance(chunk, dict):
content = chunk.get('content')
if content is not None and not any(skip in str(content) for skip in ["context window", "max_tokens", "<|im_end|>"]):
if chunk.get('type') == 'console':
# Accumulate code separately
code_buffer.append(str(content))
# Show complete message + current code
full_response = []
if message_buffer:
full_response.extend(message_buffer)
if code_buffer:
full_response.append(f"\n```python\n{''.join(code_buffer)}\n```\n")
response_placeholder.markdown(''.join(full_response))
else:
# Accumulate message until we have a complete thought
current = str(content)
message_buffer.append(current)
if '.' in current or '\n' in current or len(''.join(message_buffer)) > 80:
# Show complete message + current code
full_response = []
if message_buffer:
full_response.extend(message_buffer)
if code_buffer:
full_response.append(f"\n```python\n{''.join(code_buffer)}\n```\n")
response_placeholder.markdown(''.join(full_response))
# Store the complete response
final_response = []
if message_buffer:
final_response.extend(message_buffer)
if code_buffer:
final_response.append(f"\n```python\n{''.join(code_buffer)}\n```\n")
st.session_state.messages.append({
"role": "assistant",
"content": ''.join(final_response)
})
except Exception as e:
st.error(f"Error: {str(e)}") |