app.py

# app.py
import os
import requests
import streamlit as st
from models import get_hf_api

# Configure API
API_URL = get_hf_api()
headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}

# Page configuration
st.set_page_config(
    page_title="DeepSeek Chatbot - ruslanmv.com",
    page_icon="🤖",
    layout="centered"
)

# Initialize session state for chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Sidebar configuration
with st.sidebar:
    st.header("Model Configuration")
    st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")
    
    system_message = st.text_area(
        "System Message",
        value="You are a friendly Chatbot created by ruslanmv.com",
        height=100
    )
    
    max_tokens = st.slider(
        "Max Tokens",
        1, 4000, 512
    )
    
    temperature = st.slider(
        "Temperature",
        0.1, 4.0, 0.7
    )
    
    top_p = st.slider(
        "Top-p",
        0.1, 1.0, 0.9
    )

# Chat interface
st.title("🤖 DeepSeek Chatbot")
st.caption("Powered by Hugging Face Inference API - Configure in sidebar")

# Display chat history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Handle input
if prompt := st.chat_input("Type your message..."):
    st.session_state.messages.append({"role": "user", "content": prompt})
    
    with st.chat_message("user"):
        st.markdown(prompt)

    try:
        with st.spinner("Generating response..."):
            full_prompt = f"{system_message}\n\nUser: {prompt}\nAssistant:"
            
            # Make API request
            response = requests.post(
                API_URL,
                headers=headers,
                json={
                    "inputs": full_prompt,
                    "parameters": {
                        "max_new_tokens": max_tokens,
                        "temperature": temperature,
                        "top_p": top_p,
                        "return_full_text": False
                    }
                }
            )
            
            # Handle API errors
            if response.status_code != 200:
                error_msg = response.json().get('error', 'Unknown API error')
                st.error(f"API Error: {error_msg}")
                if "loading" in error_msg.lower():
                    st.info("Please wait a moment and try again. The model might be loading.")
                return
            
            # Process successful response
            result = response.json()
            
            if isinstance(result, list):
                # Handle normal response format
                assistant_response = result[0].get('generated_text', 'No response generated')
                
                # Clean up response
                if "Assistant:" in assistant_response:
                    assistant_response = assistant_response.split("Assistant:")[-1].strip()
                
            elif isinstance(result, dict) and 'error' in result:
                # Handle error format
                st.error(f"API Error: {result['error']}")
                return
            else:
                st.error("Unexpected response format from API")
                return

        with st.chat_message("assistant"):
            st.markdown(assistant_response)
        
        st.session_state.messages.append({"role": "assistant", "content": assistant_response})

    except Exception as e:
        st.error(f"Application Error: {str(e)}")