Update app.py

app.py

@@ -32,42 +32,16 @@ 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=0.8):
+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,
+        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)
     )
 
-# ✅ 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:
@@ -78,33 +52,25 @@ def ensure_english(text):
         return "⚠️ Language detection failed. Please ask your question again."
     return text
 
-# ✅ Main Response Function (Follow-Up Question Removed)
+# ✅ Main Response Function (Fixing Repetition & Context)
 def get_response(system_message, chat_history, user_text, max_new_tokens=800):
-    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}"
-        chat_history.append({'role': 'user', 'content': user_text})
-        chat_history.append({'role': 'assistant', 'content': response})
-        return response, chat_history, nasa_url
-
-    # ✅ Invoke Hugging Face Model
-    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.9)
-
+    # ✅ Ensure conversation history is included correctly
     filtered_history = "\n".join(f"{msg['role']}: {msg['content']}" for msg in chat_history)
 
     prompt = PromptTemplate.from_template(
-        "[INST] You are a helpful AI assistant.\n\nCurrent Conversation:\n{chat_history}\n\n"
+        "[INST] You are a knowledgeable and formal AI assistant. Please provide detailed, structured answers "
+        "without unnecessary enthusiasm or emojis.\n\n"
+        "Current Conversation:\n{chat_history}\n\n"
         "User: {user_text}.\n [/INST]\n"
-        "AI: Provide a detailed explanation with depth. Use a conversational tone. "
-        "🚨 Answer **only in English**."
-        "Ensure a friendly, engaging tone."
+        "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()
 
@@ -117,7 +83,7 @@ def get_response(system_message, chat_history, user_text, max_new_tokens=800):
     st.session_state.chat_history.append({'role': 'user', 'content': user_text})
     st.session_state.chat_history.append({'role': 'assistant', 'content': response})
 
-    return response, chat_history
+    return response, st.session_state.chat_history
 
 # ✅ Streamlit UI
 st.title("🚀 HAL - NASA AI Assistant")