Update app.py
Browse files
app.py
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@@ -3,7 +3,7 @@ from unsloth import FastLanguageModel
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import torch
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# Load the model and tokenizer
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model_name = "Rafay17/Llama3.2_1b_customModle2" #
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = FastLanguageModel.from_pretrained(
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@@ -18,38 +18,7 @@ FastLanguageModel.for_inference(model) # Call this immediately after loading th
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# Function to generate a response
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def generate_response(input_text):
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# Prepare the labeled prompt for the model
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labeled_prompt =
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"Please provide the response with the following labels:\n"
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"Speaker: [SPEAKER]\n"
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"Text: [TEXT]\n"
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"Sentiment: [SENTIMENT]\n"
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"Emotion: [EMOTION]\n"
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"Intent: [INTENT]\n"
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"Tone: [TONE]\n"
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"Confidence Level: [CONFIDENCE]\n"
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"Frustration Level: [FRUSTRATION]\n"
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"Response Length: [LENGTH]\n"
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"Action Required: [ACTION]\n"
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"Interruption: [INTERRUPTION]\n"
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"Cooperation Level: [COOPERATION]\n"
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"Clarity: [CLARITY]\n"
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"Objective: [OBJECTIVE]\n"
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"Timeline: [TIMELINE]\n"
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"Motivation: [MOTIVATION]\n"
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"Conversation Stage: [STAGE]\n"
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"Resolution: [RESOLUTION]\n"
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"Context: [CONTEXT]\n"
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"Urgency: [URGENCY]\n"
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"Problem Type: [PROBLEM]\n"
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"Key Words: [KEYWORDS]\n"
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"Expected Detail: [DETAIL]\n"
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"Time Gap: [TIME]\n"
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"Client Expectation: [EXPECTATION]\n"
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"Channel: [CHANNEL]\n"
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"Power Relationship: [POWER]\n\n"
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f"User Input: {input_text}\n"
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"Response:"
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)
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# Prepare the input for the model
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inputs = tokenizer(
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@@ -75,12 +44,13 @@ def generate_response(input_text):
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# Function to take user input and generate output
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def user_interaction():
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while True:
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user_input = input("
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if user_input.lower() == 'exit':
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print("Exiting the
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break
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print("
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generate_response(user_input)
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# Start the user interaction
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import torch
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# Load the model and tokenizer
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model_name = "Rafay17/Llama3.2_1b_customModle2" # Your model name
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = FastLanguageModel.from_pretrained(
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# Function to generate a response
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def generate_response(input_text):
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# Prepare the labeled prompt for the model
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labeled_prompt = f"User Input: {input_text}\nResponse:"
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# Prepare the input for the model
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inputs = tokenizer(
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# Function to take user input and generate output
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def user_interaction():
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print("Welcome to the Chatbot! Type 'exit' to quit.")
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while True:
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user_input = input("You: ")
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if user_input.lower() == 'exit':
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print("Exiting the chatbot. Goodbye!")
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break
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print("Chatbot is generating a response...")
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generate_response(user_input)
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# Start the user interaction
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