gradio

app.py

@@ -1,11 +1,11 @@
-import streamlit as st
+import gradio as gr
 from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from dotenv import load_dotenv
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.core import Settings
 import os
-import base64
+import tempfile
 
 # Load environment variables
 load_dotenv()
@@ -31,12 +31,6 @@ DATA_DIR = "data"
 os.makedirs(DATA_DIR, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
-def displayPDF(file):
-    with open(file, "rb") as f:
-        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
-    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
-    st.markdown(pdf_display, unsafe_allow_html=True)
-
 def data_ingestion():
     documents = SimpleDirectoryReader(DATA_DIR).load_data()
     storage_context = StorageContext.from_defaults()
@@ -69,33 +63,54 @@ def handle_query(query):
     else:
         return "Sorry, I couldn't find an answer."
 
+def process_file(file):
+    if file is None:
+        return "Please upload a PDF file."
+    
+    temp_dir = tempfile.mkdtemp()
+    temp_path = os.path.join(temp_dir, "uploaded.pdf")
+    
+    with open(temp_path, "wb") as f:
+        f.write(file.read())
+    
+    # Copy the file to the DATA_DIR
+    os.makedirs(DATA_DIR, exist_ok=True)
+    dest_path = os.path.join(DATA_DIR, "saved_pdf.pdf")
+    os.replace(temp_path, dest_path)
+    
+    # Process the uploaded PDF
+    data_ingestion()
+    
+    return "PDF processed successfully. You can now ask questions about its content."
 
-# Streamlit app initialization
-st.title("(PDF) Information and Inference🗞️")
-st.markdown("Retrieval-Augmented Generation") 
-st.markdown("start chat ...🚀")
-
-if 'messages' not in st.session_state:
-    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
-
-with st.sidebar:
-    st.title("Menu:")
-    uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button")
-    if st.button("Submit & Process"):
-        with st.spinner("Processing..."):
-            filepath = "data/saved_pdf.pdf"
-            with open(filepath, "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            # displayPDF(filepath)  # Display the uploaded PDF
-            data_ingestion()  # Process PDF every time new file is uploaded
-            st.success("Done")
+def chatbot(message, history):
+    response = handle_query(message)
+    return response
 
-user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
-if user_prompt:
-    st.session_state.messages.append({'role': 'user', "content": user_prompt})
-    response = handle_query(user_prompt)
-    st.session_state.messages.append({'role': 'assistant', "content": response})
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# (PDF) Information and Inference🗞️")
+    gr.Markdown("Retrieval-Augmented Generation")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            file_output = gr.Textbox(label="Upload Status")
+            upload_button = gr.UploadButton("Upload PDF", file_types=[".pdf"])
+            upload_button.upload(process_file, upload_button, file_output)
+        
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(
+                [],
+                elem_id="chatbot",
+                bubble_full_width=False,
+            )
+            msg = gr.Textbox(label="Ask me anything about the content of the PDF:")
+            clear = gr.Button("Clear")
+    
+    msg.submit(chatbot, [msg, chatbot], [chatbot, msg]).then(
+        lambda: gr.update(value=""), None, [msg], queue=False
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
 
-for message in st.session_state.messages:
-    with st.chat_message(message['role']):
-        st.write(message['content'])
+if __name__ == "__main__":
+    demo.launch()