Update app.py

app.py

@@ -209,83 +209,68 @@ EXAMPLES = [
 
 
 # Definir la función simple_chat
-@spaces.GPU()
-def simple_chat(message: dict, temperature: float = 0.8, max_length: int = 4096, top_p: float = 1, top_k: int = 10, penalty: float = 1.0):
-    # Cargar el modelo preentrenado
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_ID,
-        torch_dtype=torch.bfloat16,
-        low_cpu_mem_usage=True,
-        trust_remote_code=True
-    )
+# @spaces.GPU()
+# def simple_chat(message: dict, temperature: float = 0.8, max_length: int = 4096, top_p: float = 1, top_k: int = 10, penalty: float = 1.0):
+#     # Cargar el modelo preentrenado
+#     model = AutoModelForCausalLM.from_pretrained(
+#         MODEL_ID,
+#         torch_dtype=torch.bfloat16,
+#         low_cpu_mem_usage=True,
+#         trust_remote_code=True
+#     )
         
-    conversation = []
-
-    # if message.file:
-    #     file_contents = message.file.file.read()
-    #     # Aquí deberías procesar el archivo como corresponda, por ejemplo:
-    #     # choice, contents = mode_load(file_contents)
-    #     # Por ahora solo agregaremos un marcador de posición
-    #     choice = "doc"
-    #     contents = "Contenido del archivo"
-    #     if choice == "image":
-    #         conversation.append({"role": "user", "image": contents, "content": message.text})
-    #     elif choice == "doc":
-    #         format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message.text
-    #         conversation.append({"role": "user", "content": format_msg})
-    # else:
-    #     conversation.append({"role": "user", "content": message.text})
-
-    if "file" in message and message["file"]:
-        file_path = message["file"]
-        choice, contents = mode_load(file_path)
-        if choice == "image":
-            conversation.append({"role": "user", "image": contents, "content": message["text"]})
-        elif choice == "doc":
-            format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message["text"]
-            conversation.append({"role": "user", "content": format_msg})
-    else:
-        conversation.append({"role": "user", "content": message["text"]})
-
-    # Preparar entrada para el modelo
-    input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True,
-                                              return_tensors="pt", return_dict=True).to(model.device)
-    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
-
-    # Configurar parámetros de generación
-    generate_kwargs = dict(
-        max_length=max_length,
-        streamer=streamer,
-        do_sample=True,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature,
-        repetition_penalty=penalty,
-        eos_token_id=[151329, 151336, 151338],
-    )
-    gen_kwargs = {**input_ids, **generate_kwargs}
-
-    # Generar respuesta de manera asíncrona
-    def generate():
-        with torch.no_grad():
-            thread = Thread(target=model.generate, kwargs=gen_kwargs)
-            thread.start()
-            buffer = ""
-            for new_text in streamer:
-                buffer += new_text
-                yield buffer.encode('utf-8')
-
-    return StreamingResponse(generate(), media_type="text/plain")
-
-@app.post("/chat/")
-async def test_endpoint(message: dict):
-    if "text" not in message:
-        raise HTTPException(status_code=400, detail="Missing 'text' in request body")
-
-    if "file" not in message:
-        print("Sin File")
+#     conversation = []
+
+#     if "file" in message and message["file"]:
+#         file_path = message["file"]
+#         choice, contents = mode_load(file_path)
+#         if choice == "image":
+#             conversation.append({"role": "user", "image": contents, "content": message["text"]})
+#         elif choice == "doc":
+#             format_msg = contents + "\n\n\n" + "{} files uploaded.\n" + message["text"]
+#             conversation.append({"role": "user", "content": format_msg})
+#     else:
+#         conversation.append({"role": "user", "content": message["text"]})
+
+#     # Preparar entrada para el modelo
+#     input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True,
+#                                               return_tensors="pt", return_dict=True).to(model.device)
+#     streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+
+#     # Configurar parámetros de generación
+#     generate_kwargs = dict(
+#         max_length=max_length,
+#         streamer=streamer,
+#         do_sample=True,
+#         top_p=top_p,
+#         top_k=top_k,
+#         temperature=temperature,
+#         repetition_penalty=penalty,
+#         eos_token_id=[151329, 151336, 151338],
+#     )
+#     gen_kwargs = {**input_ids, **generate_kwargs}
+
+#     # Generar respuesta de manera asíncrona
+#     def generate():
+#         with torch.no_grad():
+#             thread = Thread(target=model.generate, kwargs=gen_kwargs)
+#             thread.start()
+#             buffer = ""
+#             for new_text in streamer:
+#                 buffer += new_text
+#                 yield buffer.encode('utf-8')
+
+#     return StreamingResponse(generate(), media_type="text/plain")
+
+# @app.post("/chat/")
+# async def test_endpoint(message: dict):
+#     if "text" not in message:
+#         raise HTTPException(status_code=400, detail="Missing 'text' in request body")
+
+#     if "file" not in message:
+#         print("Sin File")
     
-    return simple_chat(message)
+#     return simple_chat(message)
 
 with gr.Blocks(css=CSS, theme="soft", fill_height=True) as demo:
     gr.HTML(TITLE)