Update app.py

app.py

@@ -12,13 +12,12 @@ from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 import faiss
 import uuid
-from dotenv import load_dotenv
 
-load_dotenv()
-HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-RAG_ACCESS_KEY = os.getenv("RAG_ACCESS_KEY")
+# Load secrets from Streamlit
+HUGGINGFACEHUB_API_TOKEN = st.secrets["HUGGINGFACEHUB_API_TOKEN"]
+RAG_ACCESS_KEY = st.secrets["RAG_ACCESS_KEY"]
 
-# Session state init
+# Initialize session state
 if "vectorstore" not in st.session_state:
     st.session_state.vectorstore = None
 if "history" not in st.session_state:
@@ -26,7 +25,7 @@ if "history" not in st.session_state:
 if "authenticated" not in st.session_state:
     st.session_state.authenticated = False
 
-# Sidebar with login
+# Sidebar with logo and authentication
 with st.sidebar:
     try:
         st.image("bsnl_logo.png", width=200)
@@ -36,6 +35,7 @@ with st.sidebar:
     st.header("RAG Control Panel")
     api_key_input = st.text_input("Enter RAG Access Key", type="password")
 
+    # Custom styled Authenticate button
     st.markdown("""
         <style>
         .auth-button button {
@@ -81,7 +81,7 @@ with st.sidebar:
         st.write(f"**A{i+1}:** {a}")
         st.markdown("---")
 
-# Main UI
+# Main app interface
 def main():
     st.markdown("""
         <style>
@@ -114,10 +114,10 @@ def main():
             except Exception as e:
                 st.error(f"Error generating answer: {str(e)}")
 
-# Processing PDF
+# Process PDF and build vector store
 def process_input(input_data):
     os.makedirs("vectorstore", exist_ok=True)
-    os.chmod("vectorstore", 0o700)  # safer permissions
+    os.chmod("vectorstore", 0o777)
 
     progress_bar = st.progress(0)
     status = st.status("Processing PDF file...", expanded=True)
@@ -161,20 +161,20 @@ def process_input(input_data):
     progress_bar.progress(1.0)
     return vector_store
 
-# Q&A logic
+# Answer the user's query
 def answer_question(vectorstore, query):
     try:
         llm = HuggingFaceHub(
             repo_id="mistralai/Mistral-7B-Instruct-v0.1",
-            model_kwargs={"temperature": 0.7, "max_new_tokens": 512},
+            model_kwargs={"temperature": 0.7, "max_length": 512},
             huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
         )
     except Exception as e:
-        raise RuntimeError("Failed to load LLM. Ensure your Hugging Face API key is valid and the model is public or accessible.") from e
+        raise RuntimeError("Failed to load LLM. Check Hugging Face API key and access rights.") from e
 
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
     prompt_template = PromptTemplate(
-        template="Use the context below to answer the question.\n\nContext: {context}\n\nQuestion: {question}\n\nAnswer:",
+        template="Use the context to answer the question concisely:\n\nContext: {context}\n\nQuestion: {question}\n\nAnswer:",
         input_variables=["context", "question"]
     )
 
@@ -189,5 +189,6 @@ def answer_question(vectorstore, query):
     result = qa_chain({"query": query})
     return result["result"].split("Answer:")[-1].strip()
 
+# Run the app
 if __name__ == "__main__":
     main()