Update app.py

app.py

@@ -7,11 +7,11 @@ 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
+from langdetect import detect
 
 # ✅ Check for GPU or Default to CPU
 device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"✅ Using device: {device}")  # Debugging info
+print(f"✅ Using device: {device}")
 
 # ✅ Environment Variables
 HF_TOKEN = os.getenv("HF_TOKEN")
@@ -25,7 +25,7 @@ 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)
+# ✅ Ensure Session State Variables (Maintains Chat History)
 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:
@@ -33,15 +33,15 @@ if "response_ready" not in st.session_state:
 if "follow_up" not in st.session_state:
     st.session_state.follow_up = ""
 
-# ✅ 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=0.8):
+# ✅ Initialize Hugging Face Model (CPU/GPU Compatible)
+def get_llm_hf_inference(model_id="meta-llama/Llama-2-7b-chat-hf", max_new_tokens=800, temperature=0.6):
     return HuggingFaceEndpoint(
         repo_id=model_id,
         max_new_tokens=max_new_tokens,
         temperature=temperature,
         token=HF_TOKEN,
         task="text-generation",
-        device=-1 if device == "cpu" else 0  # ✅ Force CPU (-1) or GPU (0)
+        device=-1 if device == "cpu" else 0
     )
 
 # ✅ NASA API Function
@@ -57,7 +57,7 @@ def get_nasa_apod():
 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)
+    device=-1 if device == "cpu" else 0
 )
 
 def analyze_sentiment(user_text):
@@ -70,17 +70,19 @@ def predict_action(user_text):
         return "nasa_info"
     return "general_query"
 
-# ✅ Ensure English Responses
+# ✅ Ensure English Responses (Fixed Detection Error)
 def ensure_english(text):
+    """Ensures the response is in English, preventing false language detection errors."""
     try:
         detected_lang = detect(text)
-        if detected_lang != "en":
-            return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
+        if detected_lang == "en":
+            return text  # ✅ It's in English, return as-is
     except:
-        return "⚠️ Language detection failed. Please ask your question again."
-    return text
+        pass  # 🔥 Ignore detection errors, assume English
+    
+    return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
 
-# ✅ Follow-Up Question Generation
+# ✅ Follow-Up Question Generation (Ensures Proper Formatting)
 def generate_follow_up(user_text):
     """Generates a structured follow-up question in a concise format."""
     
@@ -90,63 +92,59 @@ def generate_follow_up(user_text):
         "Ensure it's concise and structured exactly as requested without extra commentary."
     )
 
-    hf = get_llm_hf_inference(max_new_tokens=30, temperature=0.8)  # 🔥 Lower temp for consistency
+    hf = get_llm_hf_inference(max_new_tokens=30, temperature=0.8)
     output = hf.invoke(input=prompt_text).strip()
 
-    # ✅ Extract the relevant part using regex to remove unwanted symbols or truncations
+    # ✅ Extract relevant part, removing unwanted symbols
     cleaned_output = re.sub(r"```|''|\"", "", output).strip()
 
-    # ✅ 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?"
 
     return cleaned_output
 
-# ✅ Main Response Function
+# ✅ Main Response Function (Fixed History & Language Issues)
 def get_response(system_message, user_text, max_new_tokens=800):
-    """
-    Generates a response from the chatbot, ensures conversation history is updated, and includes a follow-up question.
-    """
+    """Generates a response and ensures conversation history is updated."""
     
-    chat_history = st.session_state.chat_history  # ✅ Get Chat History Reference
+    chat_history = st.session_state.chat_history  # ✅ Get Chat History
+
+    # ✅ Store User Input in Chat History BEFORE Generating Response
+    chat_history.append({'role': 'user', 'content': user_text})
 
-    # ✅ Detect Intent (NASA query vs General AI chat)
+    # ✅ Detect Intent (NASA vs General AI chat)
     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)
 
         # ✅ Append to chat history
-        chat_history.append({'role': 'user', 'content': user_text})
         chat_history.append({'role': 'assistant', 'content': response})
         chat_history.append({'role': 'assistant', 'content': follow_up})
-        st.session_state.chat_history = chat_history  # ✅ Update Session History
+        st.session_state.chat_history = chat_history
         return response, follow_up, nasa_url
 
     # ✅ Format Conversation History for Model Input
     formatted_chat_history = "\n".join(f"{msg['role']}: {msg['content']}" for msg in chat_history)
 
     # ✅ Invoke Hugging Face Model
-    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.7)
 
-    # ✅ Define the Chat Prompt Template
     prompt = PromptTemplate.from_template(
         "[INST] You are a helpful AI assistant.\n\nCurrent Conversation:\n{chat_history}\n\n"
         "User: {user_text}.\n [/INST]\n"
-        "AI: Provide a detailed explanation with depth. Use a conversational tone. "
+        "AI: Provide a detailed explanation with depth. Use a conversational tone."
         "🚨 Answer **only in English**."
-        "Ensure a friendly, engaging tone."
         "\nHAL:"
     )
 
-    # ✅ Generate AI Response
     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=formatted_chat_history))
     response = response.split("HAL:")[-1].strip() if "HAL:" in response else response.strip()
 
+    # ✅ Prevent False Language Errors
     response = ensure_english(response)
 
     if not response:
@@ -154,43 +152,19 @@ def get_response(system_message, user_text, max_new_tokens=800):
 
     follow_up = generate_follow_up(user_text)
 
-    # ✅ Append to Chat History
-    chat_history.append({'role': 'user', 'content': user_text})
+    # ✅ Append Responses to Chat History
     chat_history.append({'role': 'assistant', 'content': response})
     chat_history.append({'role': 'assistant', 'content': follow_up})
-    st.session_state.chat_history = chat_history  # ✅ Persist History
+    st.session_state.chat_history = chat_history
 
     return response, follow_up, None
 
 # ✅ Streamlit UI
 st.title("🚀 HAL - NASA AI Assistant")
 
-# ✅ 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)
-
 # ✅ 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)
+    st.markdown(f"**{message['role'].capitalize()}**: {message['content']}")
 
 # ✅ Chat Input
 user_input = st.chat_input("Type your message here...")
@@ -199,12 +173,10 @@ if user_input:
     response, follow_up, image_url = get_response("You are a helpful AI assistant.", user_input)
 
     if response:
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {response}</div>", unsafe_allow_html=True)
+        st.markdown(f"**HAL**: {response}")
 
     if follow_up:
-        st.markdown(f"<div class='assistant-msg'><strong>HAL:</strong> {follow_up}</div>", unsafe_allow_html=True)
+        st.markdown(f"**HAL**: {follow_up}")
 
     if image_url:
         st.image(image_url, caption="NASA Image of the Day")
-
-st.session_state.response_ready = True