Update app.py

app.py

@@ -9,14 +9,11 @@ from langchain_groq import ChatGroq
 from langchain_core.prompts import PromptTemplate
 from langchain_classic.chains import RetrievalQA
 
-# Suppress unnecessary logs
+# Suppress logs
 warnings.filterwarnings("ignore")
 
 # --- CONFIGURATION ---
-# Ensure you have 'MY_GROQ_KEY' in your HF Space Secrets
 GROQ_API_KEY = os.environ.get("MY_GROQ_KEY")
-
-# High-quality embedding model (Runs on CPU)
 embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
 rag_chain = None
 
@@ -26,47 +23,30 @@ def build_rag_system(file):
     if not GROQ_API_KEY: return "❌ Error: Groq API Key missing in Secrets!"
 
     try:
-        # Load PDF or TXT
         loader = PyPDFLoader(file.name) if file.name.endswith(".pdf") else TextLoader(file.name)
         documents = loader.load()
-        
-        # Split into chunks
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=80)
         texts = text_splitter.split_documents(documents)
-        
-        # Create Vector Store
         vector_db = FAISS.from_documents(texts, embeddings)
         retriever = vector_db.as_retriever(search_kwargs={"k": 3})
         
-        # Initialize Groq Llama 3.3
-        llm = ChatGroq(
-            groq_api_key=GROQ_API_KEY, 
-            model_name="llama-3.3-70b-versatile", 
-            temperature=0
-        )
+        llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="llama-3.3-70b-versatile", temperature=0)
         
-        # Strict Prompting
-        template = """You are a professional assistant. Answer ONLY using the context. 
-        If the answer is not there, say: "I don't have enough information about this in the provided documents."
+        template = """Answer ONLY using the context. If not found, say: 
+        "I don't have enough information about this in the provided documents."
         
         Context: {context}
         Question: {question}
         Answer:"""
         
         QA_PROMPT = PromptTemplate.from_template(template)
-        rag_chain = RetrievalQA.from_chain_type(
-            llm=llm, 
-            retriever=retriever, 
-            chain_type_kwargs={"prompt": QA_PROMPT}
-        )
-        
-        return "✅ Document Vault Successfully Built!"
+        rag_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type_kwargs={"prompt": QA_PROMPT})
+        return "✅ Vault Verified & Locked!"
     except Exception as e:
         return f"❌ System Error: {str(e)}"
 
 def predict(message, history):
-    if rag_chain is None: 
-        return "Please upload a document and click Build first."
+    if rag_chain is None: return "Please upload a document first."
     try:
         res = rag_chain.invoke({"query": message})
         return res["result"]
@@ -74,25 +54,33 @@ def predict(message, history):
         return f"🚨 API ERROR: {str(e)}"
 
 # --- PROFESSIONAL UI DESIGN ---
-# Removed 'type="messages"' to fix the deployment error
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="VerityVault AI") as demo:
-    gr.Markdown("# 🛡️ VerityVault AI")
-    gr.Markdown("### Developed by: **Bilal**")
-    gr.Markdown("*Secure document intelligence with zero hallucinations.*")
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="slate", radius_size="lg"), title="VerityVault AI") as demo:
+    gr.Markdown(
+        """
+        # 🛡️ VerityVault AI
+        ### Developed by: **Bilal**
+        *Grounded document intelligence with zero hallucinations.*
+        """
+    )
     
     with gr.Row():
         with gr.Column(scale=1):
-            file_input = gr.File(label="📄 Source Document", file_types=[".pdf", ".txt"])
-            build_btn = gr.Button("🚀 BUILD VAULT", variant="primary")
+            file_input = gr.File(label="📄 Deposit Document (PDF/TXT)")
+            build_btn = gr.Button("🔒 INITIALIZE VAULT", variant="primary")
             status = gr.Textbox(label="Vault Status", interactive=False)
             
         with gr.Column(scale=2):
             gr.ChatInterface(
                 fn=predict,
-                description="The vault will only answer based on your uploaded file."
+                description="The vault only answers using your verified data."
             )
 
-build_btn.click(build_rag_system, inputs=[file_input], outputs=[status])
+    # CRITICAL: This must be indented inside the 'with gr.Blocks' block!
+    build_btn.click(
+        fn=build_rag_system, 
+        inputs=[file_input], 
+        outputs=[status]
+    )
 
 if __name__ == "__main__":
     demo.launch()