Delete app.py

app.py

@@ -1,129 +0,0 @@
-import streamlit as st
-from langchain_ollama import ChatOllama
-from langchain_core.output_parsers import StrOutputParser
-
-from langchain_core.prompts import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    ChatPromptTemplate
-)
-# Custom CSS styling
-st.markdown("""
-<style>
-    /* Existing styles */
-    .main {
-        background-color: #1a1a1a;
-        color: #ffffff;
-    }
-    .sidebar .sidebar-content {
-        background-color: #2d2d2d;
-    }
-    .stTextInput textarea {
-        color: #ffffff !important;
-    }
-    
-    /* Add these new styles for select box */
-    .stSelectbox div[data-baseweb="select"] {
-        color: white !important;
-        background-color: #3d3d3d !important;
-    }
-    
-    .stSelectbox svg {
-        fill: white !important;
-    }
-    
-    .stSelectbox option {
-        background-color: #2d2d2d !important;
-        color: white !important;
-    }
-    
-    /* For dropdown menu items */
-    div[role="listbox"] div {
-        background-color: #2d2d2d !important;
-        color: white !important;
-    }
-</style>
-""", unsafe_allow_html=True)
-st.title("🧠 DeepSeek Code Companion")
-st.caption("🚀 Your AI Pair Programmer with Debugging Superpowers")
-
-# Sidebar configuration
-with st.sidebar:
-    st.header("⚙️ Configuration")
-    selected_model = st.selectbox(
-        "Choose Model",
-        ["deepseek-r1:1.5b", "deepseek-r1:3b"],
-        index=0
-    )
-    st.divider()
-    st.markdown("### Model Capabilities")
-    st.markdown("""
-    - 🐍 Python Expert
-    - 🐞 Debugging Assistant
-    - 📝 Code Documentation
-    - 💡 Solution Design
-    """)
-    st.divider()
-    st.markdown("Built with [Ollama](https://ollama.ai/) | [LangChain](https://python.langchain.com/)")
-
-
-# initiate the chat engine
-
-llm_engine=ChatOllama(
-    model=selected_model,
-    base_url="http://localhost:11434",
-
-    temperature=0.3
-
-)
-
-# System prompt configuration
-system_prompt = SystemMessagePromptTemplate.from_template(
-    "You are an expert AI coding assistant. Provide concise, correct solutions "
-    "with strategic print statements for debugging. Always respond in English."
-)
-
-# Session state management
-if "message_log" not in st.session_state:
-    st.session_state.message_log = [{"role": "ai", "content": "Hi! I'm DeepSeek. How can I help you code today? 💻"}]
-
-# Chat container
-chat_container = st.container()
-
-# Display chat messages
-with chat_container:
-    for message in st.session_state.message_log:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-# Chat input and processing
-user_query = st.chat_input("Type your coding question here...")
-
-def generate_ai_response(prompt_chain):
-    processing_pipeline=prompt_chain | llm_engine | StrOutputParser()
-    return processing_pipeline.invoke({})
-
-def build_prompt_chain():
-    prompt_sequence = [system_prompt]
-    for msg in st.session_state.message_log:
-        if msg["role"] == "user":
-            prompt_sequence.append(HumanMessagePromptTemplate.from_template(msg["content"]))
-        elif msg["role"] == "ai":
-            prompt_sequence.append(AIMessagePromptTemplate.from_template(msg["content"]))
-    return ChatPromptTemplate.from_messages(prompt_sequence)
-
-if user_query:
-    # Add user message to log
-    st.session_state.message_log.append({"role": "user", "content": user_query})
-    
-    # Generate AI response
-    with st.spinner("🧠 Processing..."):
-        prompt_chain = build_prompt_chain()
-        ai_response = generate_ai_response(prompt_chain)
-    
-    # Add AI response to log
-    st.session_state.message_log.append({"role": "ai", "content": ai_response})
-    
-    # Rerun to update chat display
-    st.rerun()