Create app.py

app.py

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+import os
+import io
+import requests
+import numpy as np
+import faiss
+import gradio as gr
+from groq import Groq
+from sentence_transformers import SentenceTransformer
+from PyPDF2 import PdfReader
+
+# -----------------------
+# CONFIG
+# -----------------------
+# Hugging Face Space will inject GROQ_API_KEY as a secret
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+if not GROQ_API_KEY:
+    raise ValueError("❌ Missing GROQ_API_KEY. Please set it in Hugging Face Space → Settings → Repository secrets.")
+
+client = Groq(api_key=GROQ_API_KEY)
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+faiss_index = None
+chunks = []
+RELEVANCE_THRESHOLD = 0.28
+
+
+# -----------------------
+# HELPER FUNCTIONS
+# -----------------------
+def download_drive_file_bytes(drive_url: str) -> bytes:
+    """Download bytes from a Google Drive share link."""
+    file_id = drive_url.split("/d/")[1].split("/")[0]
+    download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
+    r = requests.get(download_url, timeout=30)
+    r.raise_for_status()
+    return r.content
+
+
+def pdf_bytes_to_text(pdf_bytes: bytes) -> str:
+    """Extract text from PDF bytes."""
+    try:
+        reader = PdfReader(io.BytesIO(pdf_bytes))
+        return "\n".join([page.extract_text() or "" for page in reader.pages])
+    except Exception:
+        return ""
+
+
+def chunk_text(text, chunk_size=250, overlap=50):
+    words = text.split()
+    return [" ".join(words[i : i + chunk_size]) for i in range(0, len(words), chunk_size - overlap)]
+
+
+def build_faiss_index_from_drive_links(drive_links):
+    global faiss_index, chunks
+    all_chunks = []
+    for link in drive_links:
+        try:
+            data = download_drive_file_bytes(link)
+            text = pdf_bytes_to_text(data)
+            if not text.strip():
+                try:
+                    text = data.decode("utf-8")
+                except Exception:
+                    text = ""
+            all_chunks.extend(chunk_text(text))
+        except Exception as e:
+            print(f"[Error] {link}: {e}")
+
+    if not all_chunks:
+        return "❌ No valid text found. Please check your Drive file links."
+
+    emb = embedder.encode(all_chunks, convert_to_numpy=True).astype("float32")
+    emb /= np.linalg.norm(emb, axis=1, keepdims=True)
+
+    faiss_index = faiss.IndexFlatIP(emb.shape[1])
+    faiss_index.add(emb)
+    chunks = all_chunks
+
+    return f"✅ Knowledge base ready! {len(chunks)} chunks indexed."
+
+
+def retrieve_top_k(query, k=4):
+    if faiss_index is None:
+        return [], []
+    q_emb = embedder.encode([query], convert_to_numpy=True).astype("float32")
+    q_emb /= np.linalg.norm(q_emb, axis=1, keepdims=True)
+    D, I = faiss_index.search(q_emb, k)
+    retrieved = [chunks[i] for i in I[0]]
+    return retrieved, D[0]
+
+
+def ask_groq_with_rag(query):
+    if not query.strip():
+        return "<div style='color:#b91c1c;'>Please type a question.</div>"
+
+    retrieved, scores = retrieve_top_k(query)
+    if not retrieved:
+        return "<div style='color:#b91c1c;'>Knowledge base not initialized.</div>"
+
+    if max(scores) < RELEVANCE_THRESHOLD:
+        return "<div style='border:2px solid #ef4444; border-radius:10px; padding:15px; background:#fff7f7;'><h3 style='color:#b91c1c;'>❌ Sorry, I don’t know — that’s not covered in my knowledge base.</h3></div>"
+
+    context = "\n\n---\n\n".join(retrieved)
+    prompt = f"""
+You are a helpful assistant. Use ONLY the following context to answer.
+If answer not found in context, say exactly:
+"❌ Sorry, I don’t know — that’s not covered in my knowledge base."
+
+Context:
+{context}
+
+Question:
+{query}
+"""
+
+    try:
+        resp = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3.3-70b-versatile",
+        )
+        ans = resp.choices[0].message.content.strip()
+    except Exception as e:
+        return f"<div style='color:#b91c1c;'>Error: {e}</div>"
+
+    return f"""
+    <div style="border-radius:12px; padding:16px; background:#faf5ff; border:1px solid #d8b4fe;">
+      <h3 style="color:#6b21a8;">💡 Answer from Knowledge Base</h3>
+      <div style="font-size:15px; color:#1f2937; line-height:1.45;">{ans}</div>
+    </div>
+    """
+
+
+# -----------------------
+# INITIALIZE KNOWLEDGE BASE
+# -----------------------
+DRIVE_LINKS = [
+    "https://drive.google.com/file/d/1gl_6EAvN5uzTUbir_ytOBUaSmr9pWKNF/view?usp=sharing"
+]
+
+status_msg = build_faiss_index_from_drive_links(DRIVE_LINKS)
+
+# -----------------------
+# GRADIO UI (HCI-driven)
+# -----------------------
+css = """
+body {
+  font-family: 'Inter', system-ui, sans-serif;
+  background: linear-gradient(135deg, #ede9fe, #fce7f3);
+}
+.gradio-container {max-width: 900px; margin: auto;}
+h1 {text-align:center; color:#6d28d9;}
+.status-box {
+  text-align:center;
+  font-size:16px;
+  color:#047857;
+  background:#ecfdf5;
+  border:1px solid #6ee7b7;
+  border-radius:8px;
+  padding:8px;
+}
+"""
+
+with gr.Blocks(css=css, title="RAG Knowledge Chatbot") as demo:
+    gr.Markdown("<h1>📚 RAG Knowledge Chatbot</h1>")
+    gr.Markdown("<p style='text-align:center;color:#7c3aed;'>Ask questions only from my internal knowledge base</p>")
+    gr.Markdown(f"<div class='status-box'>{status_msg}</div>")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("### 💭 Ask your question")
+            query = gr.Textbox(
+                placeholder="Type your question...",
+                lines=2,
+                show_label=False,
+            )
+            ask_btn = gr.Button("🚀 Ask", variant="primary")
+            answer_html = gr.HTML(
+                "<div style='font-size:15px;color:#374151;'>Answer will appear here...</div>"
+            )
+            ask_btn.click(ask_groq_with_rag, inputs=query, outputs=answer_html)
+        with gr.Column(scale=1):
+            gr.Markdown("### 📄 Knowledge Base Files")
+            for link in DRIVE_LINKS:
+                gr.Markdown(f"- <a href='{link}' target='_blank'>{link}</a>")
+
+demo.launch()