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Update app.py
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app.py
CHANGED
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import
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import time
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import streamlit as st
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import google.generativeai as genai
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from google.auth import default
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from dotenv import load_dotenv
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# Load
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if os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
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credentials, _ = default()
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genai.configure(credentials=credentials)
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else:
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st.error("❌ GOOGLE_APPLICATION_CREDENTIALS not set in the environment.")
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st.stop()
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#
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st.markdown("""
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<style>
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body, .stApp { background-color: #121212 !important; color: #e0e0e0 !important; }
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.stChatInput { background: #222 !important; border: 1px solid #555 !important; }
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</style>
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""", unsafe_allow_html=True)
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st.
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if "history" not in st.session_state:
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st.session_state.history = []
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# Display chat history
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for message in st.session_state.history:
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role, content = message["role"], message["content"]
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with st.chat_message(role):
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st.markdown(content)
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# Chat input
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prompt = st.chat_input("Write a sentence you'd like to improve...")
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if prompt:
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with st.chat_message("user"):
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st.markdown(prompt)
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st.session_state.history.append({"role": "user", "content": prompt})
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with st.spinner("Analyzing..."):
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try:
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full_prompt = f"""
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You are an advanced grammar assistant. Correct the given sentence and explain the changes clearly.
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Respond in the following format:
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**Correction:** <Corrected Sentence>
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**Explanation:** <Why you corrected it>
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Sentence: {prompt}
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"""
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response = model.generate_content(full_prompt)
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result = response.text
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st.session_state.history.append({"role": "assistant", "content": result})
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with st.chat_message("assistant"):
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st.markdown(result)
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except Exception as e:
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st.error(f"Error: {e}")
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from transformers import pipeline
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import streamlit as st
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# Load grammar correction model
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@st.cache_resource
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def load_model():
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return pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
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corrector = load_model()
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# Streamlit UI
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st.title("Grammar Correction Assistant")
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user_input = st.text_area("Enter a sentence to correct:", "She don't like going to the gym because it make her tired.")
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if st.button("Correct Sentence"):
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with st.spinner("Correcting..."):
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result = corrector(user_input, max_length=100, clean_up_tokenization_spaces=True)
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corrected_sentence = result[0]['generated_text']
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st.markdown(f"**Corrected Sentence:** {corrected_sentence}")
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