Update app.py

app.py

@@ -1,4 +1,3 @@
-# app.py
 import streamlit as st
 import os
 from io import BytesIO
@@ -12,10 +11,18 @@ from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 import faiss
 import uuid
+from dotenv import load_dotenv
 
-# Load secrets from Streamlit
-HUGGINGFACEHUB_API_TOKEN = st.secrets["HUGGINGFACEHUB_API_TOKEN"]
-RAG_ACCESS_KEY = st.secrets["RAG_ACCESS_KEY"]
+# Load local .env (only useful locally)
+load_dotenv()
+
+# Load keys
+RAG_ACCESS_KEY = os.getenv("RAG_ACCESS_KEY")
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN", "").strip()
+
+if not HUGGINGFACEHUB_API_TOKEN:
+    st.warning("Hugging Face API token not found in environment variables! "
+               "Please set it in your Hugging Face Secrets or your .env file.")
 
 # Initialize session state
 if "vectorstore" not in st.session_state:
@@ -25,17 +32,17 @@ if "history" not in st.session_state:
 if "authenticated" not in st.session_state:
     st.session_state.authenticated = False
 
-# Sidebar with logo and authentication
+# Sidebar with BSNL logo and authentication
 with st.sidebar:
     try:
         st.image("bsnl_logo.png", width=200)
-    except FileNotFoundError:
+    except Exception:
         st.warning("BSNL logo not found.")
 
     st.header("RAG Control Panel")
     api_key_input = st.text_input("Enter RAG Access Key", type="password")
 
-    # Custom styled Authenticate button
+    # Blue authenticate button style
     st.markdown("""
         <style>
         .auth-button button {
@@ -46,6 +53,7 @@ with st.sidebar:
             padding: 10px 20px;
             border: none;
             transition: all 0.3s ease;
+            width: 100%;
         }
         .auth-button button:hover {
             background-color: #0056b3 !important;
@@ -57,7 +65,7 @@ with st.sidebar:
     with st.container():
         st.markdown('<div class="auth-button">', unsafe_allow_html=True)
         if st.button("Authenticate"):
-            if api_key_input == RAG_ACCESS_KEY:
+            if api_key_input == RAG_ACCESS_KEY and RAG_ACCESS_KEY is not None:
                 st.session_state.authenticated = True
                 st.success("Authentication successful!")
             else:
@@ -81,7 +89,7 @@ with st.sidebar:
         st.write(f"**A{i+1}:** {a}")
         st.markdown("---")
 
-# Main app interface
+# Main app UI
 def main():
     st.markdown("""
         <style>
@@ -114,32 +122,32 @@ def main():
             except Exception as e:
                 st.error(f"Error generating answer: {str(e)}")
 
-# Process PDF and build vector store
+# PDF processing logic
 def process_input(input_data):
     os.makedirs("vectorstore", exist_ok=True)
     os.chmod("vectorstore", 0o777)
 
     progress_bar = st.progress(0)
-    status = st.status("Processing PDF file...", expanded=True)
+    status = st.empty()
 
-    status.update(label="Reading PDF file...")
+    status.text("Reading PDF file...")
     progress_bar.progress(0.2)
     pdf_reader = PdfReader(BytesIO(input_data.read()))
     documents = "".join([page.extract_text() or "" for page in pdf_reader.pages])
 
-    status.update(label="Splitting text...")
+    status.text("Splitting text...")
     progress_bar.progress(0.4)
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
     texts = text_splitter.split_text(documents)
 
-    status.update(label="Creating embeddings...")
+    status.text("Creating embeddings...")
     progress_bar.progress(0.6)
     hf_embeddings = HuggingFaceEmbeddings(
         model_name="sentence-transformers/all-mpnet-base-v2",
         model_kwargs={'device': 'cpu'}
     )
 
-    status.update(label="Building vector store...")
+    status.text("Building vector store...")
     progress_bar.progress(0.8)
     dimension = len(hf_embeddings.embed_query("test"))
     index = faiss.IndexFlatL2(dimension)
@@ -153,24 +161,24 @@ def process_input(input_data):
     uuids = [str(uuid.uuid4()) for _ in texts]
     vector_store.add_texts(texts, ids=uuids)
 
-    status.update(label="Saving vector store...")
+    status.text("Saving vector store...")
     progress_bar.progress(0.9)
     vector_store.save_local("vectorstore/faiss_index")
 
-    status.update(label="Done!", state="complete")
+    status.text("Done!")
     progress_bar.progress(1.0)
     return vector_store
 
-# Answer the user's query
+# Question-answering logic
 def answer_question(vectorstore, query):
-    try:
-        llm = HuggingFaceHub(
-            repo_id="mistralai/Mistral-7B-Instruct-v0.1",
-            model_kwargs={"temperature": 0.7, "max_length": 512},
-            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
-        )
-    except Exception as e:
-        raise RuntimeError("Failed to load LLM. Check Hugging Face API key and access rights.") from e
+    if not HUGGINGFACEHUB_API_TOKEN:
+        raise RuntimeError("Missing Hugging Face API token. Please set it in your secrets.")
+
+    llm = HuggingFaceHub(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.1",
+        model_kwargs={"temperature": 0.7, "max_length": 512},
+        huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
+    )
 
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
     prompt_template = PromptTemplate(
@@ -189,6 +197,5 @@ def answer_question(vectorstore, query):
     result = qa_chain({"query": query})
     return result["result"].split("Answer:")[-1].strip()
 
-# Run the app
 if __name__ == "__main__":
     main()