Create app.py

app.py

@@ -0,0 +1,145 @@
+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": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct",
+        "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.title("Open Interpreter REPL")
+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)}")