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app.py

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+from PyPDF2 import PdfReader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+import os
+os.environ["OPENAI_API_KEY"] = "sk-proj-FiRph57Razvp7ndTaNQ3T3BlbkFJ4cEeTjMqUaQomG961dyc"
+# provide the path of  pdf file/files.
+pdfreader = PdfReader('csp.pdf')
+from typing_extensions import Concatenate
+# read text from pdf
+raw_text = ''
+for i, page in enumerate(pdfreader.pages):
+    content = page.extract_text()
+    if content:
+        raw_text += content
+raw_text
+text_splitter = CharacterTextSplitter(
+    separator = "\n",
+    chunk_size = 800,
+    chunk_overlap  = 200,
+    length_function = len,
+)
+texts = text_splitter.split_text(raw_text)
+len(texts)
+embeddings = OpenAIEmbeddings()
+document_search = FAISS.from_texts(texts, embeddings)
+document_search
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+chain = load_qa_chain(OpenAI(), chain_type="stuff")
+import gradio as gr
+
+
+# Define the function to process the query, retrieve documents, and run the chain process
+def process_query(query):
+    # Perform similarity search to retrieve relevant documents
+    docs = document_search.similarity_search(query)
+    
+    # Run the chain process with the retrieved documents and the user query
+    response = chain.run(input_documents=docs, question=query)
+    
+    # Return the response
+    return response
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=process_query,  # Function to process user input
+    inputs="text",  # Textbox for user input
+    outputs="text",  # Textbox to display the response
+    title="Document Search and Chain Process",  # Interface title
+    description="Enter your query to search for relevant documents and run the chain process."  # Interface description
+)
+
+# Launch the interface
+iface.launch()

requirements.txt.txt

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+ langchain
+ openai
+ PyPDF2
+ faiss-cpu
+ tiktoken
+gradio