Create app.py

app.py

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+import openai
+import gradio as gr
+from langchain.chains import RetrievalQA
+from langchain.llms import openai
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import chatOpenAI
+from PyPDF import PdfReader
+
+#Function to load and process the PDF document
+def load_pdf(file):
+    #Load the PDF usign Langchain's PyPDFLoader
+    loader=PyPDFLoader(file.name)
+    documents=loader.load()
+    return documents
+
+# Summarization function using GPT-4
+def summarize_pdf(file,openai_api_key):
+    #set the openAI API key dynamically
+    openai.api_key=openai_api_key
+
+    # Load and process the PDF
+    documents=load_pdf(file)
+
+
+    # Create embeddings for the documents
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+    # Use Langchain's FAISS Vector Store to store and search the embeddings
+    vector_store=FAISS.from_documents(documents,embeddings)
+
+    # Create a RetrievalQA chain for summarization
+    llm = ChatOpenAI(model='gpt-40', openai_api_key=openai_api_key)   #passing api key here
+    qa_chain=RetrivalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vector_store.as_retriever()
+    )
+
+
+    # Query the model for a summary of the document
+    response = qa_chain.run("Summarize the content of the research paper.")
+    return response
+
+
+#Function to handle user queries and provide answers from the document
+def query_pdf(file,user_query,openai_api_key):
+    #set the openai api key dynamically
+    openai.api_key=openai_api_key
+
+    #Load and process the PDF
+    documents = load_pdf(file)
+
+    # Create embeddings for the documents
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+    # Use Langchain's FAISS vector store to store and search the embeddings
+    vector_store = FAISS.from_documents(documents, embeddings)
+
+    # Create a RetrievalQA chain for querying the document
+    llm=ChatOpenAI(model="gpt-40", openai_api_key=openai_api_key)  #passing api key here
+    qa_chain=RetrivalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vector_store.as_retriever()
+    )
+
+    # Query the model for the user query
+    response = qa_chain.run(user_query)
+    return response
+
+    # Define Gradio interface for the summarization
+    def create_gradio_interface():
+        with gr.Blocks() as demo:
+            gr.Markdown("### ChatPDF and Research Paper Summarizer using GPT-4 and Langchain ")
+
+            # Input field for API key
+            with gr.Row():
+                openai_api_key_input=gr.Textbox(label="Enter OpenAI API key",type ="password",placeholder="Enter your openai api key here")
+
+                with gr.Tab("Summarize PDF"):
+                    with gr.Row():
+                        pdf_file = gr.file(label="Upload PDF Document")
+                        summarize_btn=gr.Button("Summarize")
+                        summary_output=gr.Textbox(label="Summary",interactive=False)
+                        clear_btn_summary=gr.Button("Clear Response")
+
+
+                        #Summarize Button Logic
+                        summarize_btn.click(summarize_pdf,inputs=[pdf_file,openai_api_key_input],outputs=summary_output)
+
+                        # Clear response Button Logic for summary Tab
+                        clear_btn_summary.click(lambda:"",inputs=[],outputs=summary_output)
+
+                with gr.Tab("Ask Questions"):
+                    with gr.Row():
+                        pdf_file_q = gr.File(label="Upload PDF Document")
+                        user_input = gr.Textbox(label="Enter your question")
+                        answer_output = gr.Textbox(label="Answer",interactive=False)
+                        clear_btn_answer = gr.Button("clear Response")
+
+                    # Submit Question Logic
+                    user_input.submit(query_pdf,inputs=[pdf_file_q,user_input,openai_api_key_input],outputs=answer_output)
+
+
+                    # Clear response button logic for anser tab
+                    clear_btn_answer.click(lambda:"",inputs=[],outputs=answer_output)
+
+                    user_input.submit(None,None,answer_output)
+    return demo
+
+# Run Gradio app
+if __name__=="__main__":
+    demo = create_gradio_interface()
+    demo.launch(debug=True)
+
+
+                    
+
+                        
+
+                        
+                        
+
+
+
+    
+