Create app.py

app.py

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+import gradio as gr
+import os
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain import FAISS
+from gradio_pdf import PDF  # Import PDF from gradio_pdf
+
+# Function to process uploaded PDF and generate responses based on the document and user input
+def chat_with_pdf(pdf_file, api_key, user_question):
+    # Set the Google API key
+    os.environ["GOOGLE_API_KEY"] = api_key
+    
+    # Load the document
+    loader = PyPDFLoader(pdf_file.name)
+    pages = loader.load_and_split()
+
+    # Create a vector db index
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = FAISS.from_documents(pages, embeddings)
+    
+    # Search relevant docs based on user question
+    docs = db.similarity_search(user_question)
+    
+    # Prepare the context for the API request
+    content = "\n".join([x.page_content for x in docs])
+    qa_prompt = "Use the following pieces of context to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer.----------------"
+    input_text = qa_prompt + "\nContext:" + content + "\nUser question:\n" + user_question
+    
+    # Call Gemini API (ChatGoogleGenerativeAI) to generate a response
+    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
+    result = llm.invoke(input_text)
+    
+    # Return the bot's response (without chat history)
+    return result.content
+
+# Create a Gradio interface with a split layout
+with gr.Blocks() as iface:
+    with gr.Row():
+        with gr.Column(scale=1):
+            pdf_input = gr.File(label="Upload PDF")  # Upload PDF file
+            pdf_display = PDF(label="PDF Preview")  # PDF preview using gradio_pdf
+        with gr.Column(scale=1):
+            response_output = gr.Textbox(label="Bot Response")  # Output for the bot response
+            question_box = gr.Textbox(label="Ask a question", placeholder="Enter your question here")
+            api_key_box = gr.Textbox(label="API Key", type="password", placeholder="Enter your Google API Key here")
+    
+    # Directly display the PDF once uploaded without using the 'upload' method
+    pdf_input.change(lambda pdf_file: pdf_file.name, inputs=pdf_input, outputs=pdf_display)
+    
+    # When the user submits a question, process it and return the bot's response
+    question_box.submit(
+        chat_with_pdf, 
+        inputs=[pdf_input, api_key_box, question_box],
+        outputs=response_output
+    )
+    
+# Launch the Gradio app
+iface.launch()