Create app.py

app.py

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+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?"}]
+
+# ✅ 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
+    filtered_history = "\n".join(
+        f"{msg['role'].capitalize()}: {msg['content']}"
+        for msg in chat_history[-5:]  # ✅ Only keep the last 5 exchanges to prevent overflow
+    )
+ 
+    prompt = PromptTemplate.from_template(
+        "[INST] You are a highly knowledgeable AI assistant. Answer concisely, avoid repetition, and structure responses well."
+        "\n\nCONTEXT:\n{chat_history}\n"
+        "\nLATEST USER INPUT:\nUser: {user_text}\n"
+        "\n[END CONTEXT]\n"
+        "Assistant:"
+    )
+
+    # ✅ 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))
+    
+    # Clean up the response - remove any "HAL:" prefix if present
+    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?"
+
+    # ✅ Update conversation history
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': response})
+
+    # ✅ Keep only last 10 exchanges to prevent unnecessary repetition
+    return response, chat_history[-10:]
+
+# ✅ 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)
+
+# ✅ Chat UI
+user_input = st.chat_input("Type your message here...")
+
+if user_input:
+    # Get response and update chat history
+    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
+    )
+
+# ✅ Display chat history (ONLY display from history, not separately)
+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)