Create app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+import docx2txt
+import json
+import pandas as pd
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+# Step 2: Load environment variable
+load_dotenv()
+api_key = os.getenv("GOOGLE_API_KEY")
+
+# Step 3: Configure Google_API
+genai.configure(api_key=api_key)
+
+# Step 4: Function to read files and extract text
+def extract_text(file):
+    text = ""
+    if file.name.endswith(".pdf"):
+        pdf_reader = PdfReader(file)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    elif file.name.endswith(".docx"):
+        text = docx2txt.process(file)
+    elif file.name.endswith(".txt"):
+        text = file.read().decode("utf-8")
+    elif file.name.endswith(".csv"):
+        df = pd.read_csv(file)
+        text = df.to_string()
+    elif file.name.endswith(".xlsx"):
+        df = pd.read_excel(file)
+        text = df.to_string()
+    elif file.name.endswith(".json"):
+        data = json.load(file)
+        text = json.dumps(data, indent=4)
+    return text
+
+# Step 5: Function to convert text into chunks
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# Step 6: Function for converting chunks into embeddings and saving the FAISS index
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    
+    # Ensure the directory exists
+    if not os.path.exists("faiss_index"):
+        os.makedirs("faiss_index")
+        
+    vector_store.save_local("faiss_index")
+    print("FAISS index saved successfully.")
+
+# Step 7: Function to implement Gemini-Pro Model
+def get_conversational_chain():
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context. If the answer is not in
+    the provided context, just say, "The answer is not available in the context." Do not provide a wrong answer.\n\n
+    Context:\n {context}\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+# Step 8: Function to take inputs from user and generate response
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+    chain = get_conversational_chain()
+    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+    return response["output_text"]
+
+# Step 9: Streamlit App
+def main():
+    st.set_page_config(page_title="RAG Chatbot")
+    st.header("Chat with Multiple Files using RAG and Gemini ")
+
+    user_question = st.text_input("Ask a Question")
+
+    if user_question:
+        with st.spinner("Processing your question..."):
+            response = user_input(user_question)
+            st.write("Reply: ", response)
+
+    with st.sidebar:
+        st.title("Upload Files:")
+        uploaded_files = st.file_uploader("Upload your files", accept_multiple_files=True, type=["pdf", "docx", "txt", "csv", "xlsx", "json"])
+        if st.button("Submit & Process"):
+            if uploaded_files:
+                with st.spinner("Processing files..."):
+                    combined_text = ""
+                    for file in uploaded_files:
+                        combined_text += extract_text(file) + "\n"
+                    text_chunks = get_text_chunks(combined_text)
+                    get_vector_store(text_chunks)
+                    st.success("Files processed and indexed successfully!")
+            else:
+                st.error("Please upload at least one file.")
+
+if __name__ == "__main__":
+    main()