Update app.py

app.py

@@ -1,113 +1,95 @@
 import os
-import streamlit as st
-from langdetect import detect
+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 if GPU is available but don't load anything yet
+# ✅ 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
+# ✅ 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 with NASA-related information today?"}]
+    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
 
-if "model_loaded" not in st.session_state:
-    st.session_state.model_loaded = 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)
+    )
 
-# Load environment variables
-def load_api_keys():
-    hf_token = os.getenv("HF_TOKEN")
-    nasa_api_key = os.getenv("NASA_API_KEY")
-    
-    missing_keys = []
-    if not hf_token:
-        missing_keys.append("HF_TOKEN")
-    if not nasa_api_key:
-        missing_keys.append("NASA_API_KEY")
-    
-    return hf_token, nasa_api_key, missing_keys
-
-# Lazy-load the model only when needed
-def load_model():
-    with st.spinner("Loading AI model... This may take a moment."):
-        try:
-            from langchain_huggingface import HuggingFaceEndpoint
-            from langchain_core.prompts import PromptTemplate
-            from langchain_core.output_parsers import StrOutputParser
-            
-            hf_token, _, _ = load_api_keys()
-            
-            # Use a smaller model if you're having resource issues
-            llm = HuggingFaceEndpoint(
-                repo_id="meta-llama/Llama-2-7b-chat-hf",  # Consider a smaller model like "distilroberta-base"
-                max_new_tokens=800,
-                temperature=0.3,
-                token=hf_token,
-                task="text-generation",
-                device=-1 if device == "cpu" else 0
-            )
-            st.session_state.model_loaded = True
-            st.session_state.llm = llm
-            st.session_state.prompt = PromptTemplate.from_template(
-                "[INST] You are HAL, a NASA AI assistant with deep knowledge of space, astronomy, and NASA missions. "
-                "Answer concisely and accurately.\n\n"
-                "CONTEXT:\n{chat_history}\n"
-                "\nLATEST USER INPUT:\nUser: {user_text}\n"
-                "[END CONTEXT]\n"
-                "Assistant:"
-            )
-            return True
-        except Exception as e:
-            st.error(f"Error loading model: {str(e)}")
-            return False
-
-# Ensure English responses
+# ✅ Ensure English Responses
 def ensure_english(text):
     try:
-        if text and len(text) > 5:  # Only check if there's meaningful text
-            detected_lang = detect(text)
-            if detected_lang != "en":
-                return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
-        return text
+        detected_lang = detect(text)
+        if detected_lang != "en":
+            return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
     except:
-        return text  # Return original if detection fails
+        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:"
+    )
 
-# Get response from the model
-def get_response(user_text):
-    if not st.session_state.model_loaded:
-        if not load_model():
-            return "Sorry, I'm having trouble loading. Please try again or check your environment setup."
+    # ✅ 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))
     
-    try:
-        # Prepare conversation history
-        filtered_history = "\n".join(
-            f"{msg['role'].capitalize()}: {msg['content']}"
-            for msg in st.session_state.chat_history[-5:]
-        )
-        
-        from langchain_core.output_parsers import StrOutputParser
-        
-        # Create and invoke the chat pipeline
-        chat = st.session_state.prompt | st.session_state.llm.bind(skip_prompt=True) | StrOutputParser()
-        
-        response = chat.invoke({
-            "user_text": user_text,
-            "chat_history": filtered_history
-        })
-        
-        # Clean up response
-        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?"
-        
-        return response
-        
-    except Exception as e:
-        return f"I encountered an error: {str(e)}. Please try again with a different question."
-
-# UI Styling
+    # 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 {
@@ -118,38 +100,29 @@ st.markdown("""
         max-width: 80%;
         text-align: justify;
     }
-    .user-msg { background-color: #696969; color: white; margin-left: auto; }
+    .user-msg { background-color: #696969; color: white; }
     .assistant-msg { background-color: #333333; color: white; }
-    .container { display: flex; flex-direction: column; }
+    .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)
 
-# Main UI
-st.title("🚀 HAL - NASA AI Assistant")
+# ✅ Chat UI
+user_input = st.chat_input("Type your message here...")
 
-# Check for API keys before allowing interaction
-hf_token, nasa_api_key, missing_keys = load_api_keys()
-if missing_keys:
-    st.error(f"Missing environment variables: {', '.join(missing_keys)}. Please set them to use this application.")
-else:
-    # Chat interface
-    user_input = st.chat_input("Ask me about NASA, space missions, or astronomy...")
-    
-    if user_input:
-        # Add user message to history
-        st.session_state.chat_history.append({"role": "user", "content": user_input})
-        
-        # Get AI response
-        with st.spinner("Thinking..."):
-            response = get_response(user_input)
-            st.session_state.chat_history.append({"role": "assistant", "content": response})
-    
-    # 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)
+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)