Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,138 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from PyPDF2 import PdfReader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+import os
+from langchain_groq import ChatGroq
+from langchain.chains.question_answering import load_qa_chain
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+os.environ["GROQ_API_KEY"] = "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"  
+
+# Page config
+st.set_page_config(page_title="📄 Chat with PDF", layout="wide")
+
+# Only Light theme colors now
+bg_color = "#f0f2f6"
+app_bg = "rgba(255, 255, 255, 0.6)"
+sidebar_bg = "rgba(255, 255, 255, 0.9)"
+text_color = "#000000"
+input_bg = "rgba(240, 240, 240, 0.9)"
+accent_color = "#2AA198"
+
+# Inject CSS styles (Light theme only)
+st.markdown(f"""
+<style>
+body {{
+    background: {bg_color};
+    font-family: 'Segoe UI', sans-serif;
+}}
+.stApp {{
+    background: {app_bg};
+    backdrop-filter: blur(15px);
+    padding: 2rem;
+    border-radius: 16px;
+    margin: 2rem auto;
+    max-width: 1000px;
+    color: {text_color};
+}}
+h1, h2, h3, h4, .stRadio label, label {{
+    color: {text_color} !important;
+    font-weight: 600;
+}}
+[data-testid="stSidebar"] {{
+    background: {sidebar_bg} !important;
+    color: {text_color} !important;
+    backdrop-filter: blur(10px);
+    border-right: 1px solid rgba(0,0,0,0.1);
+}}
+[data-testid="stSidebar"] * {{
+    color: {text_color} !important;
+}}
+.stTextInput input {{
+    background-color: {input_bg};
+    color: {text_color} !important;
+    border: 1px solid #ccc;
+    border-radius: 8px;
+    padding: 0.5rem;
+}}
+.stTextInput input::placeholder {{
+    color: #777777;
+}}
+.stMarkdown, .stText, .stSubheader, .stHeader, p {{
+    color: {text_color} !important;
+}}
+.stSuccess {{
+    background-color: rgba(0, 0, 0, 0.03) !important;
+    border-left: 6px solid {accent_color};
+    padding: 1rem;
+    border-radius: 12px;
+    color: {text_color} !important;
+    font-size: 1.05rem;
+}}
+button {{
+    background-color: {accent_color} !important;
+    color: white !important;
+    border-radius: 8px;
+    padding: 0.5rem 1rem;
+    border: none;
+}}
+.stRadio > label {{
+    font-size: 1rem;
+    font-weight: 600;
+    color: {text_color} !important;
+}}
+.stRadio div[role="radiogroup"] > label {{
+    color: {text_color} !important;
+    font-weight: 500;
+}}
+</style>
+""", unsafe_allow_html=True)
+
+# Sidebar UI
+with st.sidebar:
+    st.markdown("### 📁 Upload PDF")
+    file = st.file_uploader("Upload your PDF", type="pdf")
+    st.write("Then ask your question below!")
+
+# Main UI
+st.markdown(f"<h1 style='text-align:center;'>📄✨ Chat With Your PDF</h1>", unsafe_allow_html=True)
+
+if file is not None:
+    pdf_pages = PdfReader(file)
+    text = ""
+    for page in pdf_pages.pages:
+        text += page.extract_text()
+
+    # Split the text into chunks
+    text_splitter = RecursiveCharacterTextSplitter(
+        separators=["\n"],
+        chunk_size=1000,
+        chunk_overlap=150,  
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+
+    # Generate Embeddings
+    model_name = "sentence-transformers/all-mpnet-base-v2"
+    embeddings = HuggingFaceEmbeddings(model_name=model_name)
+
+    # Create vector store
+    vector_store = FAISS.from_texts(chunks, embeddings)
+
+    # Query input
+    user_query = st.text_input("Ask a question about the file")
+    if user_query:
+        match = vector_store.similarity_search(user_query)
+        llm = ChatGroq(
+            model_name="llama-3.1-8b-instant",
+            temperature=0.0,
+            max_retries=2
+        )
+        chain = load_qa_chain(llm, chain_type="stuff")
+        response = chain.run(input_documents=match, question=user_query)
+
+        st.subheader("✅ Response")
+        st.markdown(f"<div style='color:{text_color};'>{response}</div>", unsafe_allow_html=True)
+else:
+    st.info("Please upload a PDF file to get started.")