Update app.py

app.py

@@ -0,0 +1,95 @@
+import os
+import faiss
+import pickle
+import numpy as np
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+# ==== Setup ====
+st.set_page_config(page_title="🪖 US Army RAG Assistant", layout="wide")
+
+# ==== Connect to Groq ====
+groq_api_key = os.environ.get("api_key5")
+client = Groq(api_key=groq_api_key)
+
+# ==== Load FAISS index and metadata ====
+index = faiss.read_index("army_qa_faiss.index")
+with open("army_qa_metadata.pkl", "rb") as f:
+    metadata = pickle.load(f)
+
+encoder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# ==== RAG Function ====
+def rag_query(user_query, top_k=5):
+    query_embedding = encoder.encode([user_query])
+    query_embedding = np.array(query_embedding).astype("float32")
+
+    distances, indices = index.search(query_embedding, top_k)
+
+    context_chunks = []
+    for i in indices[0]:
+        if 0 <= i < len(metadata):
+            row = metadata[i]
+            question = row["question"]
+            context_chunks.append(f"Q: {question}\nA: [answer from database]")
+
+    context = "\n---\n".join(context_chunks)
+
+    prompt = f"""You are a highly trained assistant with access to US Army Field Manual knowledge.
+Use the context below to answer the user's question.
+
+Context:
+{context}
+
+User Question:
+{user_query}
+
+Answer:"""
+
+    response = client.chat.completions.create(
+        model="deepseek-r1-distill-llama-70b",
+        messages=[{"role": "user", "content": prompt}]
+    )
+
+    return response.choices[0].message.content.strip()
+
+# ==== Streamlit UI ====
+st.title("🪖 US Army RAG Assistant")
+st.markdown("Ask tactical questions based on **US Army Field Manual** knowledge.")
+st.divider()
+
+recommended_questions = [
+    "What are the fundamentals of offensive operations?",
+    "How does the Army define mission command?",
+    "What is the purpose of a support zone?",
+    "How do units prepare for degraded and denied environments (D3SOE)?",
+    "Explain the role of reconnaissance in the offense."
+]
+
+st.markdown("💡 **Recommended Questions:**")
+for q in recommended_questions:
+    st.markdown(f"- {q}")
+
+# Chat state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# ==== Input ====
+user_input = st.text_input("Your question", placeholder="Type your question here...")
+
+if st.button("Submit") and user_input:
+    if user_input.strip().lower() == "terminate":
+        st.session_state.chat_history.append((user_input, "👋 Goodbye, soldier. Stay sharp!"))
+    else:
+        answer = rag_query(user_input)
+        st.session_state.chat_history.append((user_input, answer))
+
+if st.button("Clear Chat"):
+    st.session_state.chat_history = []
+
+# ==== Display Chat ====
+for i, (user, bot) in enumerate(st.session_state.chat_history):
+    st.markdown(f"**You:** {user}")
+    st.markdown(f"**Assistant:** {bot}")
+    st.divider()