update 2.1

.gitignore

@@ -13,6 +13,7 @@ develop-eggs/
 dist/
 downloads/
 etc/
+include/
 eggs/
 scripts/
 share/

app.py

@@ -127,7 +127,7 @@ else:
     
     elif q_input and option=="Code":
         image_file = "pro-vision-dummy.jpg"
-        image = Image.open(image_file)
+        #image = Image.open(image_file)
         with st.spinner("Processing..."):
             mod_prompt = code_prompt + pdf_text
             response = get_gemini_response(mod_prompt, q_input)

app2.py

@@ -0,0 +1,68 @@
+import os
+import google.generativeai as genai
+from langchain_google_genai import GoogleGenerativeAI, ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from dotenv import load_dotenv
+import urllib
+import warnings
+from pathlib import Path as p
+from pprint import pprint
+from text_ext import extract_text_from_pdf
+import pandas as pd
+from langchain import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+import streamlit as st
+
+import warnings
+
+# Filter out LangChainDeprecationWarning
+warnings.filterwarnings("ignore", category=Warning)
+warnings.filterwarnings("ignore", category=UserWarning)
+
+load_dotenv()
+
+GOOGLE_API_KEY=os.getenv("GOOGLE_API_KEY")
+#print(GOOGLE_API_KEY)
+
+
+
+chat_model = ChatGoogleGenerativeAI(model="gemini-pro",google_api_key=GOOGLE_API_KEY, temperature=0.2,convert_system_message_to_human=True)
+
+st.set_page_config(page_title="Gemini ChatPDF Langchain Application", layout="wide")
+question = st.chat_input(key="input", placeholder="Ask your question")
+
+
+pdf_file_path = "Uploaded\paper.pdf"
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+context = extract_text_from_pdf(pdf_file_path)
+#context = "No more today"
+context = "\n\n"+context
+#print(context)
+texts = text_splitter.split_text(context)
+#print(len(texts))
+
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001",google_api_key=GOOGLE_API_KEY)
+
+if question:
+    vector_index = Chroma.from_texts(texts, embeddings).as_retriever(search_kwargs={"k":5})
+    related_docs = vector_index.get_relevant_documents(question)
+
+    prompt_template = """
+        Answer the question with full detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+        provided context just say, try to answer it from your knowledge but don't provide the wrong answer\n\n
+        Context:\n {context}?\n
+        Question: \n{question}\n
+
+        Response:
+            """
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(chat_model, chain_type="stuff", prompt=prompt)
+    response = chain({"input_documents":related_docs, "question": question}, return_only_outputs=True)
+    
+#question = "Describe the Multi-head attention layer in detail?"
+
+    result = response
+    st.write(result["output_text"])