Update app.py

app.py

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