Update app.py

app.py

@@ -25,63 +25,6 @@ if NASA_API_KEY is None:
 # ✅ Set Up Streamlit
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
-# ✅ Initialize Session State Variables (Ensuring Chat History Persists)
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
-if "response_ready" not in st.session_state:
-    st.session_state.response_ready = False
-
-# ✅ Initialize Hugging Face Model (Explicitly Set to CPU/GPU)
-def get_llm_hf_inference(model_id="meta-llama/Llama-2-7b-chat-hf", max_new_tokens=800, temperature=0.3):
-    return HuggingFaceEndpoint(
-        repo_id=model_id,
-        max_new_tokens=max_new_tokens,
-        temperature=temperature,  # 🔥 Lowered temperature for more factual and structured responses
-        token=HF_TOKEN,
-        task="text-generation",
-        device=-1 if device == "cpu" else 0  # ✅ Force CPU (-1) or GPU (0)
-    )
-
-# ✅ Ensure English Responses
-def ensure_english(text):
-    try:
-        detected_lang = detect(text)
-        if detected_lang != "en":
-            return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
-    except:
-        return "⚠️ Language detection failed. Please ask your question again."
-    return text
-
-# ✅ Main Response Function (Fixing Repetition & Context)
-def get_response(system_message, chat_history, user_text, max_new_tokens=800):
-    # ✅ Ensure conversation history is included correctly
-   import os
-import re
-import requests
-import torch
-import streamlit as st
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from transformers import pipeline
-from langdetect import detect  # Ensure this package is installed
-
-# ✅ Check for GPU or Default to CPU
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"✅ Using device: {device}")  # Debugging info
-
-# ✅ Environment Variables
-HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
-    raise ValueError("HF_TOKEN is not set. Please add it to your environment variables.")
-
-NASA_API_KEY = os.getenv("NASA_API_KEY")
-if NASA_API_KEY is None:
-    raise ValueError("NASA_API_KEY is not set. Please add it to your environment variables.")
-
-# ✅ Set Up Streamlit
-st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
-
 # ✅ Initialize Session State Variables (Ensuring Chat History Persists)
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
@@ -196,80 +139,3 @@ for message in st.session_state.chat_history:
 st.markdown("</div>", unsafe_allow_html=True)
 
 
-prompt = PromptTemplate.from_template(
-    "[INST] You are a knowledgeable and formal AI assistant. Please provide detailed, structured answers "
-    "without unnecessary enthusiasm or emojis.\n\n"
-    "Ensure responses are structured and non-repetitive."
-    "\nPrevious Conversation:\n{chat_history}\n\n"
-    "Current Conversation:\n{chat_history}\n\n"
-    "User: {user_text}.\n [/INST]\n"
-    "AI: Provide a structured and informative response while maintaining a neutral and professional tone."
-    "Ensure your response is engaging yet clear."
-    "\nHAL:"
-    )
-
-# ✅ Invoke Hugging Face Model
-hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.3)  # 🔥 Lowered temperature
-chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
-
-    response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=filtered_history))
-    response = response.split("HAL:")[-1].strip() if "HAL:" in response else response.strip()
-
-    response = ensure_english(response)
-
-    if not response:
-        response = "I'm sorry, but I couldn't generate a response. Can you rephrase your question?"
-
-    # ✅ Preserve conversation history
-    st.session_state.chat_history.append({'role': 'user', 'content': user_text})
-    st.session_state.chat_history.append({'role': 'assistant', 'content': response})
-
-    return response, st.session_state.chat_history
-
-# ✅ Streamlit UI
-st.title("🚀 HAL - NASA AI Assistant")
-
-# ✅ Justify all chatbot responses
-st.markdown("""
-    <style>
-    .user-msg, .assistant-msg {
-        padding: 11px;
-        border-radius: 10px;
-        margin-bottom: 5px;
-        width: fit-content;
-        max-width: 80%;
-        text-align: justify;
-    }
-    .user-msg { background-color: #696969; color: white; }
-    .assistant-msg { background-color: #333333; color: white; }
-    .container { display: flex; flex-direction: column; align-items: flex-start; }
-    @media (max-width: 600px) { .user-msg, .assistant-msg { font-size: 16px; max-width: 100%; } }
-    </style>
-""", unsafe_allow_html=True)
-
-# ✅ Reset Chat Button
-if st.sidebar.button("Reset Chat"):
-    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
-    st.session_state.response_ready = False
-
-# ✅ Chat UI
-user_input = st.chat_input("Type your message here...")
-
-if user_input:
-    response, st.session_state.chat_history = get_response(
-        system_message="You are a helpful AI assistant.",
-        user_text=user_input,
-        chat_history=st.session_state.chat_history
-    )
-
-    if response:
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
-
-# ✅ Display chat history
-st.markdown("<div class='container'>", unsafe_allow_html=True)
-for message in st.session_state.chat_history:
-    if message["role"] == "user":
-        st.markdown(f"<div class='user-msg'><strong>You:</strong> {message['content']}</div>", unsafe_allow_html=True)
-    else:
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {message['content']}</div>", unsafe_allow_html=True)
-st.markdown("</div>", unsafe_allow_html=True)