Streamlit's main file

src/streamlit_app.py

@@ -1,40 +1,132 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from dotenv import load_dotenv
+from  PDFprocess_sample import process_pdf
 
-"""
-# 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])),
-    ))
+# Loading GROQ and Google API
+load_dotenv()
+
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+os.environ["GOOGLE_API_KEY"]= os.getenv('GOOGLE_API_KEY')
+
+#Loading CSS files
+
+def load_css(file_name):
+    with open(file_name) as f:
+        css = f.read()
+    st.markdown(f"<style>{css}</style>", unsafe_allow_html=True)
+
+load_css('CSS/style.css')
+
+#setting up LLM
+llm = ChatGroq(
+    api_key=GROQ_API_KEY,
+    model_name="Llama3-8b-8192"
+)
+
+
+prompt = ChatPromptTemplate.from_template(
+    """
+    Answer the questions based on the provided context only.
+    Please provide the most accurate response based on the question. Try to answer in detail in 1500 words
+    <context>
+    {context}
+    <context>
+    Questions: {input}
+    """
+)
+
+input_method = st.sidebar.selectbox("Choose a method" , ["Choose input method...","Interact with Doc", "Get Ques from Doc"])
+
+
+
+st.sidebar.title("Upload your pdf")
+
+main_placeholder = st.empty()
+#Document upload
+uploaded_file = st.sidebar.file_uploader("_____________________________________", type="pdf", accept_multiple_files=True)
+st.sidebar.write("Press Submit to process:")
+process = st.sidebar.button("Submit")
+
+#Document processing to convert it into vectors
+if process:
+    if uploaded_file:
+        # Process the uploaded PDF file
+        process_pdf(uploaded_file)
+    else:
+        st.warning("Please upload a PDF file.")
+        
+if input_method == "Choose input method...":
+    st.title(f"Welcome You all!")
+    st.title("Choose an option in the sidebar")
+    st.title("Now, let's get started!") 
+    
+        
+#If User wants to interact with the document
+elif input_method == "Interact with Doc":
+    st.title(f"let's Interact with pdf's")
+
+    prompt1 = st.text_input("______", placeholder="Enter your Question")
+
+
+    # Generate response if question is entered
+    if prompt1 and "vectors" in st.session_state:
+        document_chain = create_stuff_documents_chain(llm, prompt)
+        retriever = st.session_state.vectors.as_retriever()
+        retrieval_chain = create_retrieval_chain(retriever, document_chain)
+        
+        
+        response = retrieval_chain.invoke({'input': prompt1})
+
+        # st.write(response['answer'])
+        
+        #Get the respose in the card
+        st.markdown(
+        f"""
+        <div class="card">
+            <div class="response">{response['answer']}</div>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+        
+        
+        
+#When User wants to get questions from the doc based on certain topic
+elif input_method == "Get Ques from Doc":
+    st.title(f"Let's Get Ques from Document")
+    
+    prompt2 = """Based on the topic of {topic}, 
+                kindly provide a comprehensive list of all possible questions that could arise. 
+                For each question, provide detailed and explanatory answers in atleast 1000 words detail based on the context,
+                ensuring that the responses are as informative as possible.
+                make sure you strictly follow the {topic}"""
+    topic = st.text_input("Enter a topic", placeholder="What is your topic")
+    
+    # Generate response if question is entered
+    if topic and "vectors" in st.session_state:
+        document_chain = create_stuff_documents_chain(llm, prompt)
+        retriever = st.session_state.vectors.as_retriever()
+        retrieval_chain = create_retrieval_chain(retriever, document_chain)
+        
+        
+        response = retrieval_chain.invoke({'input': prompt2})
+        
+        #Get the respose in the card
+        st.markdown(
+        f"""
+        <div class="card">
+            <div class="response">{response['answer']}</div>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+