Update app.py

app.py

@@ -1,11 +1,9 @@
-# @title Think Paraguayo
+# Think Paraguayo
 
 import os
 import random
 import time
 
-# os.system("pip install gradio, llama_index, ragatouille, llama-cpp-python")
-# os.system("git clone https://github.com/EnPaiva93/think-paraguayo-space-aux.git")
 os.system("wget https://huggingface.co/thinkPy/gua-a_v0.2-dpo_mistral-7b_GGUF/resolve/main/gua-a_v0.2-dpo_mistral-7b_q4_K_M.gguf -O model.gguf")
 
 from llama_cpp import Llama
@@ -14,7 +12,7 @@ from ragatouille import RAGPretrainedModel
 from llama_index.core import Document, SimpleDirectoryReader
 from llama_index.core.node_parser import SentenceSplitter
 
-max_seq_length = 512 # Choose any! We auto support RoPE Scaling internally!
+max_seq_length = 256 
 
 prompt = """Responde a preguntas de forma clara, amable, concisa y solamente en el lenguaje español, sobre el libro Ñande Ypykuéra.
 Contexto
@@ -43,50 +41,21 @@ Contexto
 
 # Initialize the LLM
 llm = Llama(model_path="model.gguf",
-            n_ctx=512,
+            n_ctx=max_seq_length,
             n_threads=2)
 
-DOC_PATH = "/home/user/app/think-paraguayo-space-aux/index" 
+DOC_PATH = "/home/user/app/index" 
 
-print(os.listdir())
-
-documents = SimpleDirectoryReader(input_files=["libro.txt"]).load_data()
-
-parser = SentenceSplitter(chunk_size=128, chunk_overlap=64)
-nodes = parser.get_nodes_from_documents(
-    documents, show_progress=False
-)
-list_nodes = [node.text for node in nodes]
-
-print(os.getcwd())
-
-# if os.path.exists(DOC_PATH):
 RAG = RAGPretrainedModel.from_pretrained("AdrienB134/ColBERTv2.0-spanish-mmarcoES")
 RAG = RAG.from_index(DOC_PATH, n_gpu=None)
 RAG.search("init", None, k=1)
 
-# else:
-#   RAG = RAGPretrainedModel.from_pretrained("AdrienB134/ColBERTv2.0-spanish-mmarcoES")
-#   my_documents = list_nodes
-#   index_path = RAG.index(index_name=DOC_PATH, max_document_length= 100, collection=my_documents)
-
-# def convert_list_to_dict(lst):
-#   res_dct = {i: lst[i] for i in range(len(lst))}
-#   return res_dct
-
 def reformat_rag(results_rag):
   if results_rag is not None:
     return [result["content"] for result in results_rag]
   else:
     return [""]
 
-# def response(query: str = "Quien es gua'a?", context: str = ""):
-#   # print(base_prompt.format(query,""))
-#   inputs = tokenizer([base_prompt.format(query,"")], return_tensors = "pt").to("cuda")
-#   outputs = model.generate(**inputs, max_new_tokens = 128, temperature = 0.1, repetition_penalty=1.15, pad_token_id=tokenizer.eos_token_id)
-#   return tokenizer.batch_decode(outputs[0][inputs["input_ids"].shape[1]:].unsqueeze(0), skip_special_tokens=True)[0]
-
-
 def chat_stream_completion(message, history):
 
     context = reformat_rag(RAG.search(message, None, k=1))
@@ -98,50 +67,17 @@ def chat_stream_completion(message, history):
     response = llm.create_completion(
         prompt=full_prompt,
         temperature=0.01,
-        max_tokens=256,
+        max_tokens=max_seq_length,
         stream=True
     )
-
-    # print(response)
     
     message_repl = ""
     for chunk in response:
       if len(chunk['choices'][0]["text"]) != 0:
-        # print(chunk)
         message_repl = message_repl + chunk['choices'][0]["text"]
       yield message_repl
 
 
-# def answer_question(pipeline, character, question):
-# def answer_question(question):
-#   # context = reformat_rag(RAG.search(question, k=2))
-#   # context = " \n ".join(context)
-#   yield chat_stream_completion(question, None)
-
-# def answer_question(question):
-#   context = reformat_rag(RAG.search(question, k=2))
-#   context = " \n ".join(context)
-#   return response(question, "")
-
-# def random_element():
-#         return random.choice(list_nodes)
-
-# clear_output()
-print("Importación Completada.. OK")
-
-css = """
-    h1 {
-        font-size: 32px;
-        text-align: center;
-    }
-    h2 {
-        text-align: center;
-    }
-    img {
-        height: 750px;  /* Reducing the image height */
-    }
-    """
-
 def launcher():
     with gr.Blocks(css=css) as demo:
         gr.Markdown("# Think Paraguayo")
@@ -152,36 +88,15 @@ def launcher():
                 gr.Image(value="think_paraguayo.jpeg", type="filepath", label="Imagen Estática")
 
             with gr.Column(scale=1):
-                # with gr.Row():
-                #     button = gr.Button("Cuentame ...")
-                # with gr.Row():
-
-                #     textbox = gr.Textbox(label="", interactive=False, value=random_element())
-                #     button.click(fn=random_element, inputs=[], outputs=textbox)
-
-                # with gr.Row():
+                
                 chatbot = gr.ChatInterface(
                     fn=chat_stream_completion,
                     retry_btn = None,
                     stop_btn = None,
                     undo_btn = None
                 ).queue()
-                # with gr.Row():
-                #     msg = gr.Textbox()
-                # with gr.Row():
-                #     clear = gr.ClearButton([msg, chatbot])
-
-                # def respond(message, chat_history):
-                #     bot_message = answer_question(message)
-                #     print(bot_message)
-                #     chat_history.append((message, bot_message))
-                #     time.sleep(2)
-                #     return "", chat_history
-
-                # msg.submit(chat_stream_completion, [msg, chatbot], [msg, chatbot])
-
 
-    demo.launch()#(share=True, inline= False, debug=True)
+    demo.launch()
 
 if __name__ == "__main__":
     launcher()