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Update app.py
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app.py
CHANGED
@@ -86,20 +86,20 @@ def generate_response(user_input, chat_history):
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logger.info("Generating response for user input...")
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global total_water_consumption
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#
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input_water_consumption = calculate_water_consumption(user_input, True)
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total_water_consumption += input_water_consumption
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#
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conversation_history = ""
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if chat_history:
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for message in chat_history:
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# Remove any [INST] tags from the history
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user_msg = message[0].replace("[INST]", "").replace("[/INST]", "").strip()
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assistant_msg = message[1].replace("[INST]", "").replace("[/INST]", "").strip()
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conversation_history += f"
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prompt
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logger.info("Generating model response...")
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outputs = model_gen(
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@@ -107,41 +107,20 @@ def generate_response(user_input, chat_history):
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max_new_tokens=256,
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return_full_text=False,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1
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)
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logger.info("Model response generated successfully")
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#
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assistant_response = outputs[0]['generated_text'].strip()
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assistant_response = assistant_response.replace("[INST]", "").replace("[/INST]", "").strip()
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# If the response is too short, try to generate a more detailed one
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if len(assistant_response.split()) < 10:
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prompt += "\nPlease provide a more detailed answer with context and explanation."
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outputs = model_gen(
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prompt,
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max_new_tokens=256,
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return_full_text=False,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1
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)
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assistant_response = outputs[0]['generated_text'].strip()
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assistant_response = assistant_response.replace("[INST]", "").replace("[/INST]", "").strip()
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#
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output_water_consumption = calculate_water_consumption(assistant_response, False)
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total_water_consumption += output_water_consumption
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#
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chat_history.append([user_input, assistant_response])
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#
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water_message = f"""
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<div style="position: fixed; top: 20px; right: 20px;
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background-color: white; padding: 15px;
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@@ -164,6 +143,7 @@ def generate_response(user_input, chat_history):
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chat_history.append([user_input, error_message])
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return chat_history, show_water
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# Constants for water consumption calculation
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WATER_PER_TOKEN = {
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"input_training": 0.0000309,
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logger.info("Generating response for user input...")
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global total_water_consumption
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# Calcula el consumo de agua para la entrada
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input_water_consumption = calculate_water_consumption(user_input, True)
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total_water_consumption += input_water_consumption
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# Construye el historial de conversaci贸n sin etiquetas `[INST]`
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conversation_history = ""
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if chat_history:
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for message in chat_history:
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user_msg = message[0].replace("[INST]", "").replace("[/INST]", "").strip()
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assistant_msg = message[1].replace("[INST]", "").replace("[/INST]", "").strip()
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conversation_history += f"{user_msg} {assistant_msg} "
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# Crea el prompt sin las etiquetas `[INST]`
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prompt = f"{system_message}\n\n{conversation_history}{user_input}"
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logger.info("Generating model response...")
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outputs = model_gen(
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max_new_tokens=256,
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return_full_text=False,
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pad_token_id=tokenizer.eos_token_id,
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)
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logger.info("Model response generated successfully")
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# Limpia la salida del modelo
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assistant_response = outputs[0]['generated_text'].replace("[INST]", "").replace("[/INST]", "").strip()
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# Calcula el consumo de agua para la salida
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output_water_consumption = calculate_water_consumption(assistant_response, False)
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total_water_consumption += output_water_consumption
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# Actualiza el historial de chat
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chat_history.append([user_input, assistant_response])
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# Prepara el mensaje de consumo de agua
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water_message = f"""
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<div style="position: fixed; top: 20px; right: 20px;
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background-color: white; padding: 15px;
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chat_history.append([user_input, error_message])
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return chat_history, show_water
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# Constants for water consumption calculation
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WATER_PER_TOKEN = {
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"input_training": 0.0000309,
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