Create app.py

app.py

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+import os
+import requests
+import warnings
+import gradio as gr
+from groq import Groq
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+
+# Suppress technical warnings for a clean logs
+warnings.filterwarnings("ignore")
+
+# --- 1. CONFIGURATION & SECRETS ---
+# On Hugging Face, set 'MY_GROQ_SECRET' in Settings > Variables and Secrets
+GROQ_API_KEY = os.environ.get("MY_GROQ_SECRET")
+client = Groq(api_key=GROQ_API_KEY)
+
+# YOUR HIDDEN LINKS (Never shown in UI)
+GDRIVE_LINKS = [
+    "https://drive.google.com/file/d/10D3uJqBYG9gMWsNHcpTW4I6BKmA2otfH/view?usp=sharing"
+]
+
+# --- 2. KNOWLEDGE BASE INITIALIZATION ---
+def download_gdrive_pdf(url, output_path):
+    try:
+        file_id = url.split('/')[-2]
+        download_url = f'https://drive.google.com/uc?export=download&id={file_id}'
+        response = requests.get(download_url)
+        if response.status_code == 200:
+            with open(output_path, 'wb') as f:
+                f.write(response.content)
+            return True
+    except:
+        return False
+    return False
+
+# Initialize the vector database on startup
+all_chunks = []
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=150)
+
+for i, link in enumerate(GDRIVE_LINKS):
+    filename = f"doc_{i}.pdf"
+    if download_gdrive_pdf(link, filename):
+        try:
+            loader = PyPDFLoader(filename)
+            all_chunks.extend(text_splitter.split_documents(loader.load()))
+        finally:
+            if os.path.exists(filename):
+                os.remove(filename)
+
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vector_db = FAISS.from_documents(all_chunks, embeddings)
+
+# --- 3. STRICT RAG LOGIC ---
+def respond(message, history):
+    # Retrieve relevant snippets
+    docs = vector_db.similarity_search(message, k=5)
+    context = "\n\n".join([doc.page_content for doc in docs])
+    
+    # Strict instructions: No outside knowledge allowed
+    system_prompt = f"""
+    You are a professional Knowledge Assistant. 
+    1. Answer ONLY using the context provided.
+    2. If the answer is NOT in the context, say: "Answer not found in provided documents."
+    3. Do not mention the context or the technical nature of the search.
+    
+    CONTEXT:
+    {context}
+    """
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": message}
+        ],
+        model="llama-3.3-70b-versatile",
+        temperature=0.1,
+    )
+    return chat_completion.choices[0].message.content
+
+# --- 4. ATTRACTIVE MODERN UI ---
+custom_css = """
+body { background-color: #0f172a; }
+.gradio-container { max-width: 850px !important; margin: auto; padding-top: 50px; }
+#title-text { text-align: center; color: #38bdf8; font-weight: 800; margin-bottom: 5px; }
+#desc-text { text-align: center; color: #94a3b8; margin-bottom: 25px; }
+.chat-container { border-radius: 20px !important; border: 1px solid #334155 !important; background: #1e293b !important; box-shadow: 0 20px 50px rgba(0,0,0,0.4); }
+.primary-btn { background: linear-gradient(135deg, #38bdf8, #818cf8) !important; border: none !important; color: white !important; }
+footer { display: none !important; }
+"""
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="sky"), css=custom_css) as demo:
+    gr.HTML("<h1 id='title-text'>🌀 DocuVortex</h1>")
+    # REPLACE "User's Research AI" with your own name here!
+    gr.HTML("<p id='desc-text'>User's Research AI: Strict Document Knowledge Base</p>")
+    
+    with gr.Column(elem_id="chat-container"):
+        gr.ChatInterface(
+            fn=respond,
+            chatbot=gr.Chatbot(height=550, bubble_full_width=False, show_label=False),
+            textbox=gr.Textbox(placeholder="Ask a question about the document...", container=False, scale=7),
+            submit_btn=gr.Button("Ask AI", variant="primary", elem_classes="primary-btn"),
+            retry_btn=None,
+            undo_btn=None,
+            clear_btn=gr.Button("New Chat", variant="secondary")
+        )
+
+if __name__ == "__main__":
+    demo.launch()