Update app.py

app.py

@@ -1,52 +1,56 @@
 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,
-)
+from huggingface_hub import InferenceClient
 
-#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,
-)
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
 
-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()
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+"""
+https://docs.llamaindex.ai/en/stable/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm/
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)