Update app.py

app.py

@@ -1,64 +1,104 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-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})
-
-    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
-
-
-"""
-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)",
-        ),
+from datasets import load_dataset
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
+import torch
+from threading import Thread
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+import time
+
+ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+art_dataset= load_dataset("hichri-mo/arxiver-1000",revision="embedded")
+
+data = art_dataset["train"]
+data = data.add_faiss_index("embeddings")
+
+
+model_id= "Qwen/Qwen2.5-3B-Instruct"
+
+# use quantization to lower GPU usage
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=bnb_config
+)
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+SYS_PROMPT = """You are an assistant for answering questions.
+You are given the extracted parts of a long document and a question. Provide a conversational answer.
+If you don't know the answer, just say "I do not know." Don't make up an answer."""
+
+
+def format_prompt(prompt,retrieved_documents,k):
+  """using the retrieved documents we will prompt the model to generate our responses"""
+  PROMPT = f"Question: {prompt}\nContext: \n"
+  for idx in range(k) :
+    PROMPT+= f"{retrieved_documents['markdown'][idx]}\n"
+  return PROMPT
+
+def generate(formatted_prompt):
+  formatted_prompt = formatted_prompt[:2000] # to avoid GPU OOM
+  messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
+  # tell the model to generate
+  input_ids = tokenizer.apply_chat_template(
+      messages,
+      add_generation_prompt=True,
+      return_tensors="pt"
+  ).to(model.device)
+  # Check if terminators contain None and replace with tokenizer.eos_token_id
+  eos_token_id = terminators[0]  # Default to tokenizer.eos_token_id
+  if terminators[1] is not None:
+    eos_token_id = terminators[1]  # Use "<|eot_id|>" if it exists
+  
+  outputs = model.generate(
+      input_ids,
+      max_new_tokens=1024,
+      eos_token_id=eos_token_id, # Pass a single integer value
+      do_sample=True,
+      temperature=0.6,
+      top_p=0.9,
+  )
+  response = outputs[0][input_ids.shape[-1]:]
+  return tokenizer.decode(response, skip_special_tokens=True)
+
+def rag_chatbot(prompt:str,k:int=2):
+  scores , retrieved_documents = search(prompt, k)
+  formatted_prompt = format_prompt(prompt,retrieved_documents,k)
+  return generate(formatted_prompt)
+
+
+
+
+def rag_chatbot_interface(prompt, k):
+  return rag_chatbot(prompt, k)
+
+iface = gr.Interface(
+    fn=rag_chatbot_interface,
+    inputs=[
+        gr.Textbox(label="Enter your question"),
+        gr.Slider(minimum=1, maximum=10, step=1, value=2, label="Number of documents to retrieve")
     ],
+    outputs=gr.Textbox(label="Response"),
+    title="Chatbot with RAG",
+    description="Ask questions and get answers based on retrieved documents."
 )
 
+iface.launch()
+
+
+
+
+
+
+
 
-if __name__ == "__main__":
-    demo.launch()