Update app.py

app.py

@@ -1,95 +1,93 @@
-"""
-Install the Google AI Python SDK
-
-$ pip install google-generativeai
-
-See the getting started guide for more information:
-https://ai.google.dev/gemini-api/docs/get-started/python
-"""
-import streamlit as st
 import os
-import time
-from dotenv import load_dotenv
-
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import streamlit as st
 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
 
 load_dotenv()
+os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-def upload_to_gemini(path, mime_type=None):
-  """Uploads the given file to Gemini.
-
-  See https://ai.google.dev/gemini-api/docs/prompting_with_media
-  """
-  file = genai.upload_file(path, mime_type=mime_type)
-  print(f"Uploaded file '{file.display_name}' as: {file.uri}")
-  return file
-
-def wait_for_files_active(files):
-  """Waits for the given files to be active.
-
-  Some files uploaded to the Gemini API need to be processed before they can be
-  used as prompt inputs. The status can be seen by querying the file's "state"
-  field.
-
-  This implementation uses a simple blocking polling loop. Production code
-  should probably employ a more sophisticated approach.
-  """
-  print("Waiting for file processing...")
-  for name in (file.name for file in files):
-    file = genai.get_file(name)
-    while file.state.name == "PROCESSING":
-      print(".", end="", flush=True)
-      time.sleep(10)
-      file = genai.get_file(name)
-    if file.state.name != "ACTIVE":
-      raise Exception(f"File {file.name} failed to process")
-  print("...all files ready")
-  print()
-
-# Create the model
-# See https://ai.google.dev/api/python/google/generativeai/GenerativeModel
-generation_config = {
-  "temperature": 1,
-  "top_p": 0.95,
-  "top_k": 64,
-  "max_output_tokens": 8192,
-  "response_mime_type": "text/plain",
-}
-
-model = genai.GenerativeModel(
-  model_name="gemini-1.5-pro",
-  generation_config=generation_config,
-  # safety_settings = Adjust safety settings
-  # See https://ai.google.dev/gemini-api/docs/safety-settings
-  system_instruction="You are an expert economist. Provide concise answers and if you do not know, just say you do not know.",
-)
-
-# TODO Make these files available on the local file system
-# You may need to update the file paths
-files = [
-  upload_to_gemini("2024_25_Annex_Budget.pdf", mime_type="application/pdf"),
-  upload_to_gemini("2024_25_Budget_Speech.pdf", mime_type="application/pdf"),
-]
-
-# Some files have a processing delay. Wait for them to be ready.
-wait_for_files_active(files)
-
-chat_session = model.start_chat(
-  history=[  ]
-)
-
-response = chat_session.send_message("INSERT_INPUT_HERE")
-
-print(response.text)
+# read all pdf files and return text
+
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+# split text into chunks
+
+
+def get_text_chunks(text):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=10000, chunk_overlap=1000)
+    chunks = splitter.split_text(text)
+    return chunks  # list of strings
+
+# get embeddings for each chunk
+
+
+def get_vector_store(chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(
+        model="models/embedding-001")  # type: ignore
+    vector_store = FAISS.from_texts(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
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = ChatGoogleGenerativeAI(model="gemini-pro",
+                                   client=genai,
+                                   temperature=0.3,
+                                   )
+    prompt = PromptTemplate(template=prompt_template,
+                            input_variables=["context", "question"])
+    chain = load_qa_chain(llm=model, chain_type="stuff", prompt=prompt)
+    return chain
+
 
 def clear_chat_history():
     st.session_state.messages = [
         {"role": "assistant", "content": "upload some pdfs and ask me a question"}]
 
+
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(
+        model="models/embedding-001")  # type: ignore
+
+    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, )
+
+    print(response)
+    return response
+
+
 def main():
     st.set_page_config(
-        page_title="Budget 2024-2025 Chatbot",
+        page_title="Gemini PDF Chatbot",
         page_icon="🤖"
     )