Changes

app.py

@@ -1,66 +1,68 @@
-import streamlit as st
-import os
-import tempfile
-from dotenv import load_dotenv
-from langchain_groq import ChatGroq
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from Datapreprocessing import PreprocessingData
-from pdfparsing import ExtractDatafrompdf
-
-load_dotenv()
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-
-st.set_page_config(page_title="📄 Chat with PDF", layout="wide")
-
-# Sidebar for PDF Upload
-st.sidebar.title("📂 Upload your PDF")
-uploaded_file = st.sidebar.file_uploader("Choose a PDF", type="pdf")
-
-# LLM and Embeddings - cached
-@st.cache_resource
-def get_embeddings():
-    return HuggingFaceEmbeddings()
-
-@st.cache_resource
-def get_llm():
-    return ChatGroq(api_key=GROQ_API_KEY, model="gemma2-9b-it", temperature=0.2)
-
-# Build Retrieval Chain
-def get_chain(retriever):
-    llm = get_llm()
-    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff")
-
-# PDF processing pipeline
-def process_pdf_and_create_chain(uploaded_file):
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-        tmp.write(uploaded_file.read())
-        tmp_path = tmp.name
-
-    documents = ExtractDatafrompdf(tmp_path)
-    chunks = PreprocessingData(documents)
-    embedder = get_embeddings()
-    retriever = FAISS.from_documents(chunks, embedder).as_retriever(search_type="similarity", search_kwargs={"k": 1})
-    return get_chain(retriever)
-
-# Main UI
-st.title("📄 Ask Questions About Your PDF")
-
-if uploaded_file:
-    if "chain" not in st.session_state:
-        st.success("PDF uploaded successfully! Processing...")
-        with st.spinner("Extracting and chunking PDF..."):
-            st.session_state.chain = process_pdf_and_create_chain(uploaded_file)
-        st.success("Ready to chat with your PDF!")
-    else:
-        st.sidebar.info("Using cached PDF session.")
-
-    user_query = st.text_input("Ask a question about your PDF:")
-    if user_query:
-        with st.spinner("Generating answer..."):
-            result = st.session_state.chain.invoke({"query": user_query})
-            st.markdown("### 📌 Answer:")
-            st.write(result["result"])
-else:
-    st.info("📤 Upload a PDF from the sidebar to begin.")
+import streamlit as st
+import os
+import tempfile
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from Datapreprocessing import PreprocessingData
+from pdfparsing import ExtractDatafrompdf
+
+load_dotenv()
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+st.set_page_config(page_title="📄 Chat with PDF", layout="wide")
+
+# Sidebar for PDF Upload
+st.sidebar.title("📂 Upload your PDF")
+uploaded_file = st.sidebar.file_uploader("Choose a PDF", type="pdf")
+
+# LLM and Embeddings - cached
+@st.cache_resource
+def get_embeddings():
+    return HuggingFaceEmbeddings()
+
+@st.cache_resource
+def get_llm():
+    return ChatGroq(api_key=GROQ_API_KEY, model="gemma2-9b-it", temperature=0.2)
+
+# Build Retrieval Chain
+def get_chain(retriever):
+    llm = get_llm()
+    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff")
+
+# PDF processing pipeline
+def process_pdf_and_create_chain(uploaded_file):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+        tmp.write(uploaded_file.read())
+        tmp_path = tmp.name
+
+    documents = ExtractDatafrompdf(tmp_path)
+    chunks = PreprocessingData(documents)
+    embedder = get_embeddings()
+    retriever = FAISS.from_documents(chunks, embedder).as_retriever(search_type="similarity", search_kwargs={"k": 1})
+    return get_chain(retriever)
+
+# Main UI
+st.title("📄 Ask Questions About Your PDF")
+
+if uploaded_file:
+    if "chain" not in st.session_state:
+        st.success("PDF uploaded successfully! Processing...")
+        with st.spinner("Extracting and chunking PDF..."):
+            st.session_state.chain = process_pdf_and_create_chain(uploaded_file)
+        st.success("Ready to chat with your PDF!")
+    else:
+        st.sidebar.info("Using cached PDF session.")
+
+    user_query = st.text_input("Ask a question about your PDF:")
+    submit = st.button("Search")
+    if submit:
+        if user_query:
+            with st.spinner("Generating answer..."):
+                result = st.session_state.chain.invoke({"query": user_query})
+                st.markdown("### 📌 Answer:")
+                st.write(result["result"])
+else:
+    st.info("📤 Upload a PDF from the sidebar to begin.")