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Update app.py
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app.py
CHANGED
@@ -1,8 +1,8 @@
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import streamlit as st
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from transformers import pipeline
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import torch
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import csv
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import re
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import warnings
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warnings.filterwarnings("ignore")
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@@ -14,18 +14,17 @@ MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that
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@@ Response
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"""
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# Function to generate response
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def generate_response(instruction):
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prompt = MAGICODER_PROMPT.format(instruction=instruction)
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result =
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response = result[0]["generated_text"]
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response_start_index = response.find("@@ Response") + len("@@ Response")
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response = response[response_start_index:].strip()
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@@ -37,20 +36,14 @@ def save_to_csv(data, filename):
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writer = csv.writer(csvfile)
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writer.writerow(data)
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# Function to process user feedback
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def process_output(correct_output):
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if correct_output.lower() == 'yes':
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feedback = st.text_input("Do you want to provide any feedback?")
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save_to_csv(["Correct", feedback], 'output_ratings.csv')
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else:
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correct_code = st.text_area("Please enter the correct code:")
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feedback = st.text_input("Any other feedback you want to provide:")
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save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
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# Streamlit app
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def main():
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st.title("Magicoder Assistant")
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instruction = st.text_area("Enter your instruction here:")
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if st.button("Generate Response"):
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generated_response = generate_response(instruction)
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@@ -58,7 +51,13 @@ def main():
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st.text(generated_response)
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correct_output = st.radio("Is the generated output correct?", ("Yes", "No"))
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if __name__ == "__main__":
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main()
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import streamlit as st
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from transformers import pipeline
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import csv
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import re
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import torch
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import warnings
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warnings.filterwarnings("ignore")
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@@ Response
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"""
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@st.cache(allow_output_mutation=True)
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def load_model():
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return pipeline(
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model="ise-uiuc/Magicoder-S-DS-6.7B",
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task="text-generation"
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)
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# Function to generate response
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def generate_response(instruction):
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prompt = MAGICODER_PROMPT.format(instruction=instruction)
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result = model(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
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response = result[0]["generated_text"]
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response_start_index = response.find("@@ Response") + len("@@ Response")
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response = response[response_start_index:].strip()
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writer = csv.writer(csvfile)
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writer.writerow(data)
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# Streamlit app
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def main():
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global model
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st.title("Magicoder Assistant")
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if 'model' not in globals():
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model = load_model()
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instruction = st.text_area("Enter your instruction here:")
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if st.button("Generate Response"):
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generated_response = generate_response(instruction)
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st.text(generated_response)
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correct_output = st.radio("Is the generated output correct?", ("Yes", "No"))
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if correct_output.lower() == 'yes':
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feedback = st.text_input("Do you want to provide any feedback?")
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save_to_csv(["Correct", feedback], 'output_ratings.csv')
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else:
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correct_code = st.text_area("Please enter the correct code:")
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feedback = st.text_input("Any other feedback you want to provide:")
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save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
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if __name__ == "__main__":
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main()
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