Update app.py

app.py

@@ -6,31 +6,32 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.llms import HuggingFaceHub
 from langchain.text_splitter import CharacterTextSplitter
 import cassio
-from dotenv import load_dotenv
 import os
+import uuid
+from dotenv import load_dotenv
 
+# 🔐 Load secrets from environment (Hugging Face Spaces uses HF Secrets)
 load_dotenv()
-
 ASTRA_DB_APPLICATION_TOKEN = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
 ASTRA_DB_ID = os.getenv("ASTRA_DB_ID")
 HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 
-# === Streamlit UI Setup ===
-st.set_page_config(page_title="Query PDF with Free Hugging Face Models", layout="wide")
-st.title("📄💬 Query PDF using LangChain + AstraDB (Free Hugging Face Models)")
+# 🧠 Initialize AstraDB
+cassio.init(token=ASTRA_DB_APPLICATION_TOKEN, database_id=ASTRA_DB_ID)
 
-# === File Upload ===
+# 🎨 Streamlit UI Setup
+st.set_page_config(page_title="Query PDF with LangChain", layout="wide")
+st.title("📄💬 Query PDF using LangChain + AstraDB (Hugging Face Models)")
+
+# 📁 PDF Upload
 uploaded_file = st.file_uploader("Upload your PDF", type=["pdf"])
 
 if uploaded_file:
-    st.success("✅ PDF uploaded successfully!")
+    st.success("✅ PDF uploaded successfully.")
     process_button = st.button("🔄 Process PDF")
 
     if process_button:
-        # Initialize AstraDB
-        cassio.init(token=ASTRA_DB_APPLICATION_TOKEN, database_id=ASTRA_DB_ID)
-
-        # Read PDF contents
+        # 🧾 Read PDF
         pdf_reader = PdfReader(uploaded_file)
         raw_text = ""
         for page in pdf_reader.pages:
@@ -38,59 +39,56 @@ if uploaded_file:
             if content:
                 raw_text += content
 
-        # Split text into chunks
+        # ✂️ Split into Chunks
         text_splitter = CharacterTextSplitter(
             separator="\n", chunk_size=800, chunk_overlap=200, length_function=len
         )
         texts = text_splitter.split_text(raw_text)
 
-        # === Embeddings ===
-        embedding = HuggingFaceEmbeddings(
-            model_name="sentence-transformers/all-MiniLM-L6-v2"
-        )
+        # 🧠 Embeddings
+        embedding = HuggingFaceEmbeddings(model_name="intfloat/e5-base-v2")
 
-        # === Hugging Face LLM ===
+        # 🤖 LLM
         llm = HuggingFaceHub(
             repo_id="mistralai/Mistral-7B-Instruct-v0.1",
             huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
             model_kwargs={"temperature": 0.5, "max_new_tokens": 512}
         )
 
-        # === Create vector store and index ===
+        # 🗃️ Unique Table Name for Each PDF Upload
+        table_name = "qa_" + str(uuid.uuid4()).replace("-", "_")
+
+        # 📦 Vector Store Setup
         vector_store = Cassandra(
             embedding=embedding,
-            table_name="qa_mini_demo",
+            table_name=table_name,
             session=None,
             keyspace=None,
         )
+
         vector_store.add_texts(texts[:50])
         st.success(f"📚 {len(texts[:50])} chunks embedded and stored in AstraDB.")
 
+        # 🔍 Setup Index
         astra_vector_index = VectorStoreIndexWrapper(vectorstore=vector_store)
 
-        # === Ask Questions ===
+        # 💬 Ask Questions
         st.header("🤖 Ask a question about your PDF")
         user_question = st.text_input("💬 Type your question here")
 
         if user_question:
-           with st.spinner("🧠 Thinking..."):
-              try:
-                # Optional: show what documents are retrieved before sending to LLM
-                retrieved_docs = vector_store.similarity_search(user_question, k=4)
-                if not retrieved_docs:
-                   st.warning("⚠️ No relevant text chunks found for this question. Try a different question.")
-                else:
-                   st.markdown("### 🔍 Top Relevant Chunks (raw):")
-                   for i, doc in enumerate(retrieved_docs, 1):
-                     st.code(doc.page_content[:300], language="markdown")
-
-                 
-                     answer = astra_vector_index.query(user_question, llm=llm)
-                     if answer.strip():
-                        st.markdown("### 🧠 Answer:")
-                        st.write(answer.strip())
-                     else:
-                        st.warning("⚠️ The model returned an empty response. Try rephrasing the question or check your model/API key.")
-              except Exception as e:
-                     st.error(f"🚨 Error while generating response:\n\n{str(e)}")
-
+            with st.spinner("🧠 Thinking..."):
+                try:
+                    # Retrieve relevant context (used internally, not displayed)
+                    retrieved_docs = vector_store.similarity_search(user_question, k=8)
+                    if not retrieved_docs:
+                        st.warning("⚠️ No relevant text found. Try rephrasing your question.")
+                    else:
+                        answer = astra_vector_index.query(user_question, llm=llm)
+                        if answer.strip():
+                            st.markdown("### 🧠 Answer:")
+                            st.write(answer.strip())
+                        else:
+                            st.warning("⚠️ Model returned an empty response.")
+                except Exception as e:
+                    st.error(f"🚨 Error: {str(e)}")