Create app.py

app.py

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+# =========================================
+# 1️⃣ Install imports
+# =========================================
+import os
+import pickle
+import faiss
+import numpy as np
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+# =========================================
+# 2️⃣ Load Groq API from HF Secrets
+# =========================================
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+GROQ_MODEL = "llama-3.3-70b-versatile"
+
+# =========================================
+# 3️⃣ Load FAISS + Chunks
+# =========================================
+index = faiss.read_index("faiss_index.bin")
+
+with open("chunks.pkl", "rb") as f:
+    all_chunks = pickle.load(f)
+
+# Load embedding model (only for query embedding)
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# =========================================
+# 4️⃣ Groq Query Function
+# =========================================
+def groq_query(prompt):
+    completion = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are a strict university information assistant. Answer ONLY from provided context. Do not guess."},
+            {"role": "user", "content": prompt}
+        ],
+        model=GROQ_MODEL,
+        temperature=0
+    )
+    return completion.choices[0].message.content
+
+# =========================================
+# 5️⃣ RAG Function
+# =========================================
+def rag_answer(query, k=3):
+
+    if not query.strip():
+        return "Please enter a valid question."
+
+    query_embedding = embedding_model.encode([query])
+    query_embedding = query_embedding / np.linalg.norm(query_embedding, axis=1, keepdims=True)
+    query_embedding = query_embedding.astype("float32")
+
+    distances, indices = index.search(query_embedding, k)
+
+    retrieved_texts = [all_chunks[i] for i in indices[0]]
+
+    context = "\n\n".join(retrieved_texts)
+
+    prompt = f"""
+You must answer ONLY using the information provided below.
+If answer is not in the context, reply exactly:
+"The information is not available in the provided documents."
+
+Context:
+{context}
+
+Question:
+{query}
+"""
+
+    return groq_query(prompt)
+
+# =========================================
+# 6️⃣ Gradio UI
+# =========================================
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎓 Baltistan Academic Assistant")
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Ask your question...")
+
+    def respond(message, history):
+        answer = rag_answer(message)
+        history = history + [(message, answer)]
+        return history, ""
+
+    msg.submit(respond, [msg, chatbot], [chatbot, msg])
+
+demo.launch()