Update app.py

app.py

@@ -1,106 +1,204 @@
 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
+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")
-NASA_API_KEY = os.getenv("NASA_API_KEY")
-
-if not HF_TOKEN:
+if HF_TOKEN is None:
     raise ValueError("HF_TOKEN is not set. Please add it to your environment variables.")
 
-if not NASA_API_KEY:
+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 Streamlit
+# ✅ Set Up Streamlit
 st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")
 
-# ✅ Ensure Session State for Chat History
+# ✅ 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
+if "follow_up" not in st.session_state:
+    st.session_state.follow_up = ""
 
-# ✅ Define AI Model
+# ✅ Initialize Hugging Face Model (Explicitly Set to CPU/GPU)
 def get_llm_hf_inference(model_id="mistralai/Mistral-7B-Instruct-v0.3", max_new_tokens=512, temperature=0.7):
     return HuggingFaceEndpoint(
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
         temperature=temperature,
         token=HF_TOKEN,
-        task="text-generation"
+        task="text-generation",
+        device=-1 if device == "cpu" else 0  # ✅ Force CPU (-1) or GPU (0)
     )
 
-# ✅ Generate Follow-Up Question (Preserving Format)
+# ✅ NASA API Function
+def get_nasa_apod():
+    url = f"https://api.nasa.gov/planetary/apod?api_key={NASA_API_KEY}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        return data.get("url", ""), data.get("title", ""), data.get("explanation", "")
+    return "", "NASA Data Unavailable", "I couldn't fetch data from NASA right now."
+
+# ✅ Sentiment Analysis (Now Uses Explicit Device)
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
+    device=-1 if device == "cpu" else 0  # ✅ Force CPU (-1) or GPU (0)
+)
+
+def analyze_sentiment(user_text):
+    result = sentiment_analyzer(user_text)[0]
+    return result['label']
+
+# ✅ Intent Detection
+def predict_action(user_text):
+    if "NASA" in user_text.lower() or "space" in user_text.lower():
+        return "nasa_info"
+    return "general_query"
+
+# ✅ 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
+
+# ✅ Follow-Up Question Generation
 def generate_follow_up(user_text):
+    """Generates a structured follow-up question in a concise format."""
+    
     prompt_text = (
         f"Given the user's question: '{user_text}', generate a SHORT follow-up question in the format: "
-        "'Would you like to learn more about [related topic] or explore something else?'."
-        "Ensure it is concise and strictly follows this format."
+        "'Would you like to learn more about [related topic] or explore something else?'. "
+        "Ensure it's concise and structured exactly as requested without extra commentary."
     )
-    
-    hf = get_llm_hf_inference(max_new_tokens=30, temperature=0.6)
+
+    hf = get_llm_hf_inference(max_new_tokens=30, temperature=0.6)  # 🔥 Lower temp for consistency
     output = hf.invoke(input=prompt_text).strip()
 
+    # ✅ Extract the relevant part using regex to remove unwanted symbols or truncations
     cleaned_output = re.sub(r"```|''|\"", "", output).strip()
 
-    return cleaned_output if "Would you like to learn more about" in cleaned_output else "Would you like to explore another related topic or ask about something else?"
+    # ✅ Ensure output is formatted correctly
+    if "Would you like to learn more about" not in cleaned_output:
+        cleaned_output = "Would you like to explore another related topic or ask about something else?"
 
-# ✅ Get AI Response and Maintain Chat History
-def get_response(user_text):
-    """Generates a response and updates chat history."""
-    
-    hf = get_llm_hf_inference(max_new_tokens=512, temperature=0.9)
+    return cleaned_output
+
+# ✅ Main Response Function
+def get_response(system_message, chat_history, user_text, max_new_tokens=512):
+    action = predict_action(user_text)
+
+    # ✅ Handle NASA-Specific Queries
+    if action == "nasa_info":
+        nasa_url, nasa_title, nasa_explanation = get_nasa_apod()
+        response = f"**{nasa_title}**\n\n{nasa_explanation}"
+        follow_up = generate_follow_up(user_text)
+        chat_history.extend([
+            {'role': 'user', 'content': user_text},
+            {'role': 'assistant', 'content': response},
+            {'role': 'assistant', 'content': follow_up}
+        ])
+        return response, follow_up, chat_history, nasa_url
 
-    # Format chat history for context
-    filtered_history = "\n".join(f"{msg['role']}: {msg['content']}" for msg in st.session_state.chat_history)
+    # ✅ Invoke Hugging Face Model
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
+
+    filtered_history = "\n".join(f"{msg['role']}: {msg['content']}" for msg in chat_history)
 
-    # Create prompt
     prompt = PromptTemplate.from_template(
-        "[INST] You are a helpful AI assistant.\n\nCurrent Conversation:\n{chat_history}\n\n"
+        "[INST] {system_message}\n\nCurrent Conversation:\n{chat_history}\n\n"
         "User: {user_text}.\n [/INST]\n"
-        "AI: Provide a detailed but concise explanation with depth. "
-        "Ensure a friendly, engaging tone."
+        "AI: Provide a detailed explanation. Use a conversational tone. "
+        "🚨 Answer **only in English**."
         "\nHAL:"
     )
 
     chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
-    response = chat.invoke(input=dict(user_text=user_text, chat_history=filtered_history))
+    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()
 
-    # Generate follow-up question
+    response = ensure_english(response)
+
+    if not response:
+        response = "I'm sorry, but I couldn't generate a response. Can you rephrase your question?"
+
     follow_up = generate_follow_up(user_text)
 
-    # ✅ Preserve conversation history
-    st.session_state.chat_history.append({'role': 'user', 'content': user_text})
-    st.session_state.chat_history.append({'role': 'assistant', 'content': response})
-    st.session_state.chat_history.append({'role': 'assistant', 'content': follow_up})
+    chat_history.extend([
+        {'role': 'user', 'content': user_text},
+        {'role': 'assistant', 'content': response},
+        {'role': 'assistant', 'content': follow_up}
+    ])
 
-    return response, follow_up
+    return response, follow_up, chat_history, None
 
-# ✅ Chat UI
+# ✅ Streamlit UI
 st.title("🚀 HAL - NASA AI Assistant")
 
-# ✅ Display Conversation History BEFORE User Input
-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)
+# ✅ Justify all chatbot responses
+st.markdown("""
+    <style>
+    .user-msg, .assistant-msg {
+        padding: 10px;
+        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
+    st.session_state.follow_up = ""
 
-# ✅ User Input
+# ✅ Chat UI
 user_input = st.chat_input("Type your message here...")
 
 if user_input:
-    response, follow_up = get_response(user_input)
+    response, follow_up, st.session_state.chat_history, image_url = 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)
+
+    if follow_up:
+        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {follow_up}</div>", unsafe_allow_html=True)
+
+    if image_url:
+        st.image(image_url, caption="NASA Image of the Day")
 
-    # ✅ Display AI response
-    st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
+    st.session_state.response_ready = True
 
-    # ✅ Display Follow-Up Question
-    st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {follow_up}</div>", unsafe_allow_html=True)
+if st.session_state.response_ready and st.session_state.follow_up:
+    st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {st.session_state.follow_up}</div>", unsafe_allow_html=True)
+    st.session_state.response_ready = False