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 PyPDF2 import PdfReader
+
+
+def load_pdf(file):
+    loader=PyPDFLoader(file.name)
+    documents=loader.load()
+    return documents
+
+
+def summarize_pdf(file, openai_api_key):
+    openai.api_key=openai_api_key
+    documents=load_pdf(file)
+    embeddings=OpenAIEmbeddings(openai_api_key=openai_api_key)
+    vector_store=FAISS.from_documents(documents,embeddings)
+    llm=ChatOpenAI(model="gpt-4o", openai_api_key=openai_api_key)
+    qa_chain=RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vector_store.as_retriever()
+    )
+    
+    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-4o", openai_api_key=openai_api_key) # Passing API key here
+    qa_chain = RetrievalQA.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 Answer Tab
+            clear_btn_answer.click(lambda: "", inputs=[], outputs=answer_output)
+
+            user_input.submit(None, None, answer_output)  # Clear answer when typing new query
+
+    return demo
+
+# Run Gradio app
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch(debug=True)
+
+
+
+                
+