Update app.py

app.py

@@ -1,3 +1,4 @@
+import gradio as gr
 import os
 import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
@@ -10,13 +11,12 @@ from langchain.document_loaders import PDFMinerLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 import chromadb
-import gradio as gr
-from gradio.components import File
+import tempfile
 
 # Define Chroma Settings
 CHROMA_SETTINGS = {
     "chroma_db_impl": "duckdb+parquet",
-    "persist_directory": "db",
+    "persist_directory": tempfile.mkdtemp(),  # Use a temporary directory
     "anonymized_telemetry": False
 }
 
@@ -64,22 +64,21 @@ def qa_llm():
     
     return qa
 
-def process_answer(file):
-    db = data_ingestion(file)
-    question = input("Please enter your question: ")
+def process_answer(file, instruction):
+    # Ingest the data from the uploaded PDF
+    data_ingestion(file.name)
+    # Process the question
     qa = qa_llm()
-    generated_text = qa(question)
+    generated_text = qa(instruction)
     answer = generated_text["result"]
     return answer
 
-# Create a Gradio interface
-demo = gr.Interface(
-    fn=process_answer,
-    inputs=File(type="pdf"),
-    outputs="text",
-    title="Chatbot",
-    description="Please enter your question:"
+# Define Gradio interface
+iface = gr.Interface(
+    fn=process_answer, 
+    inputs=["file", "text"], 
+    outputs="text"
 )
 
-# Launch the Gradio interface
-demo.launch()
+# Launch the interface
+iface.launch()