Create app_llama_index.py

app_llama_index.py

@@ -0,0 +1,52 @@
+import gradio as gr
+# from transformers import pipeline
+# from transformers.utils import logging
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+import torch
+from llama_index.core import VectorStoreIndex
+from llama_index.core import Document
+from llama_index.core import Settings
+from llama_index.llms.huggingface import (
+    HuggingFaceInferenceAPI,
+    HuggingFaceLLM,
+)
+
+#system_sr = "Zoveš se U-Chat AI asistent i pomažeš korisniku usluga kompanije United Group. Korisnik postavlja pitanje ili problem, upareno sa dodatnima saznanjima. Na osnovu toga napiši korisniku kratak i ljubazan odgovor koji kompletira njegov zahtev ili mu daje odgovor na pitanje. "
+# " Ako ne znaš odgovor, reci da ne znaš, ne izmišljaj ga."
+#system_sr += "Usluge kompanije United Group uključuju i kablovsku mrežu za digitalnu televiziju, pristup internetu, uređaj EON SMART BOX za TV sadržaj, kao i fiksnu telefoniju."
+
+system_propmpt = "You are a friendly Chatbot."
+
+# "facebook/blenderbot-400M-distill",  facebook/blenderbot-400M-distill , BAAI/bge-small-en-v1.5
+Settings.llm = HuggingFaceLLM(model_name="stabilityai/stablelm-zephyr-3b",
+                              device_map="auto",
+                              system_prompt = system_propmpt,
+                              context_window=4096,
+                              max_new_tokens=256,
+#                              stopping_ids=[50278, 50279, 50277, 1, 0],
+                              generate_kwargs={"temperature": 0.5, "do_sample": False},
+#                              tokenizer_kwargs={"max_length": 4096},
+                              tokenizer_name="stabilityai/stablelm-zephyr-3b", 
+                             )
+
+Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
+documents = [Document(text="Indian parliament elections happened in April-May 2024. BJP Party won."),
+             Document(text="Indian parliament elections happened in April-May 2021. XYZ Party won."),
+             Document(text="Indian parliament elections happened in 2020. ABC Party won."),
+            ]
+index = VectorStoreIndex.from_documents(
+    documents,
+)
+
+query_engine = index.as_query_engine()
+def rag(input_text, file):
+    return query_engine.query(
+        input_text
+    )
+
+iface = gr.Interface(fn=rag, inputs=[gr.Textbox(label="Question", lines=6), gr.File()], 
+                     outputs=[gr.Textbox(label="Result", lines=6)], 
+                     title="Answer my question",
+                     description= "CoolChatBot"
+                    )
+iface.launch()