Add application file

app.py

@@ -0,0 +1,43 @@
+import os
+import pandas as pd
+import gradio as gr
+from persian_rag import PersianRAG
+
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+
+
+# Function to load CSV and initialize PersianRAG
+def init_rag(knowledge_file, embedding_model, llm_model, device, retrieved_docs):
+    knowledge = pd.read_csv(knowledge_file)
+    rag_system = PersianRAG(knowledge, embedding_model=embedding_model, llm_model=llm_model, device=device,
+                            retrieved_docs=retrieved_docs)
+    return rag_system
+
+
+# Function to handle querying
+def query_rag(rag_system, query):
+    return rag_system.rag(query)
+
+
+# Gradio interface to upload CSV and configure RAG system
+def rag_interface(knowledge_file, query, embedding_model, llm_model, device, retrieved_docs):
+    rag_system = init_rag(knowledge_file, embedding_model, llm_model, device, retrieved_docs)
+    return query_rag(rag_system, query)
+
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=rag_interface,
+    inputs=[
+        gr.File(label="Upload Knowledge Base CSV"),
+        gr.Textbox(label="Enter your query"),
+        gr.Dropdown(choices=["LABSE", "paraphrase-multilingual-mpnet-base-v2"], value="LABSE", label="Embedding Model"),
+        gr.Textbox(value="MehdiHosseiniMoghadam/AVA-Mistral-7B-V2", label="LLM Model Name"),
+        gr.Dropdown(choices=["cuda", "cpu"], value="cuda", label="Device"),
+        gr.Slider(minimum=1, maximum=5, step=1, value=3, label="Number of Retrieved Documents")],
+    outputs="text",
+    title="Persian RAG System",
+    description="Upload a CSV file as the knowledge base, ask a question, and get an answer.")
+
+# Launch the Gradio interface
+interface.launch(share=False)