Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import torch
+import csv
+import re
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# Define a prompt template for Magicoder with placeholders for instruction and response.
+MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+@@ Instruction
+{instruction}
+@@ Response
+"""
+
+# Create a text generation pipeline using the Magicoder model, text-generation task, bfloat16 torch data type and auto device mapping.
+generator = pipeline(
+    model="ise-uiuc/Magicoder-S-DS-6.7B",
+    task="text-generation",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# Function to generate response
+def generate_response(instruction):
+    prompt = MAGICODER_PROMPT.format(instruction=instruction)
+    result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
+    response = result[0]["generated_text"]
+    response_start_index = response.find("@@ Response") + len("@@ Response")
+    response = response[response_start_index:].strip()
+    return response
+
+# Function to append data to a CSV file
+def save_to_csv(data, filename):
+    with open(filename, 'a', newline='') as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(data)
+
+# Function to process user feedback
+def process_output(correct_output):
+    if correct_output.lower() == 'yes':
+        feedback = st.text_input("Do you want to provide any feedback?")
+        save_to_csv(["Correct", feedback], 'output_ratings.csv')
+    else:
+        correct_code = st.text_area("Please enter the correct code:")
+        feedback = st.text_input("Any other feedback you want to provide:")
+        save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
+
+# Streamlit app
+def main():
+    st.title("Magicoder Assistant")
+
+    instruction = st.text_area("Enter your instruction here:")
+    if st.button("Generate Response"):
+        generated_response = generate_response(instruction)
+        st.text("Generated response:")
+        st.text(generated_response)
+
+        correct_output = st.radio("Is the generated output correct?", ("Yes", "No"))
+        process_output(correct_output)
+
+if __name__ == "__main__":
+    main()