Update app.py

app.py

@@ -1,6 +1,7 @@
-from fastapi import FastAPI, File, UploadFile
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+import streamlit as st
 from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 import google.generativeai as genai
 from langchain.vectorstores import FAISS
@@ -8,32 +9,40 @@ 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
-import os
 
 load_dotenv()
+os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-app = FastAPI()
+
+
+
+
 
 def get_pdf_text(pdf_docs):
-    text = ""
+    text=""
     for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
+        pdf_reader= PdfReader(pdf)
         for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
+            text+= page.extract_text()
+    return  text
+
+
 
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
     chunks = text_splitter.split_text(text)
     return chunks
 
+
 def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     vector_store.save_local("faiss_index")
 
+
 def get_conversational_chain():
+
     prompt_template = """
     Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
     provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
@@ -41,25 +50,57 @@ def get_conversational_chain():
     Question: \n{question}\n
     Answer:
     """
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+
+    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
 
-@app.post("/upload/")
-async def upload_pdfs(files: list[UploadFile]):
-    pdf_docs = [await file.read() for file in files]
-    raw_text = get_pdf_text(pdf_docs)
-    text_chunks = get_text_chunks(raw_text)
-    get_vector_store(text_chunks)
-    return {"message": "PDFs processed and vector store created."}
-
-@app.post("/ask/")
-async def ask_question(question: str):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(question)
+
+
+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": question}, return_only_outputs=True)
-    return {"response": response["output_text"]}
 
+    
+    response = chain(
+        {"input_documents":docs, "question": user_question}
+        , return_only_outputs=True)
+
+    print(response)
+    st.write("Reply: ", response["output_text"])
+
+
+
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Chat with PDF using Gemini💁")
+    
+
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        user_input(user_question)
+
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+
+
+
+if __name__ == "__main__":
+    main()