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Update app.py
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app.py
CHANGED
@@ -18,10 +18,10 @@ if not hf_token:
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login(token=hf_token)
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# Initialize session state for timer
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if 'timer_started' not in st.session_state:
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st.session_state.timer_started = False
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if 'timer_frozen' not in st.session_state:
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st.session_state.timer_frozen = False
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# Timer component using HTML and JavaScript
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def timer():
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@@ -57,79 +57,90 @@ st.write("This model will score your reviews in your CSV file and generate a rep
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# Load models with caching to avoid reloading on every run
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@st.cache_resource
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def load_models():
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return score_pipe, gemma_pipe
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score_pipe, gemma_pipe = load_models()
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# Input: Query text for scoring and CSV file upload for candidate reviews
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query_input = st.text_area("Enter your query text for analysis (this does not need to be part of the CSV):")
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uploaded_file = st.file_uploader("Upload Reviews CSV File (must contain a 'reviewText' column)", type=["csv"])
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st.error("CSV must contain a 'reviewText' column.")
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else:
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candidate_docs = df['reviewText'].dropna().astype(str).tolist()
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except Exception as e:
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st.error(f"Error reading CSV file: {e}")
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if st.button("Generate Report"):
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# Reset timer state so that the timer always shows up
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st.session_state.timer_started = False
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st.session_state.timer_frozen = False
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# Display the timer every time
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html(timer(), height=50)
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if uploaded_file is None:
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st.error("Please upload a CSV file.")
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elif not candidate_docs:
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st.error("CSV must contain a 'reviewText' column.")
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elif not query_input.strip():
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st.error("Please enter a query text!")
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else:
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if not st.session_state.timer_started and not st.session_state.timer_frozen:
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st.session_state.timer_started = True
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html(timer(), height=50)
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status_text = st.empty()
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progress_bar = st.progress(0)
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try:
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for doc in candidate_docs:
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combined_text = f"Query: {query_input} Document: {doc}"
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result = score_pipe(combined_text)[0]
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scored_docs.append((doc, result["score"]))
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progress_bar.progress(50)
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# Stage 2: Generate Report using Gemma, including query and scored results.
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status_text.markdown("**π Generating report with Gemma...**")
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prompt = f"""
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Generate a detailed report based on the following analysis.
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Query:
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"{query_input}"
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Candidate Reviews with their scores:
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{scored_docs}
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Please provide a concise summary report explaining the insights derived from these scores.
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"""
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report = gemma_pipe(prompt, max_length=200)
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progress_bar.progress(100)
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status_text.success("**β
Generation complete!**")
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html("<script>localStorage.setItem('freezeTimer', 'true');</script>", height=0)
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st.session_state.timer_frozen = True
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st.write("**Scored Candidate Reviews:**", scored_docs)
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st.write("**Generated Report:**", report[0]['generated_text'])
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except Exception as e:
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login(token=hf_token)
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# Initialize session state for timer
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#if 'timer_started' not in st.session_state:
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#st.session_state.timer_started = False
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#if 'timer_frozen' not in st.session_state:
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#st.session_state.timer_frozen = False
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# Timer component using HTML and JavaScript
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def timer():
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# Load models with caching to avoid reloading on every run
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@st.cache_resource
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@st.cache_resource
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def load_models():
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try:
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score_pipe = pipeline("text-classification", model="mixedbread-ai/mxbai-rerank-base-v1", device=0)
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except Exception as e:
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st.error(f"Error loading score model: {e}")
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score_pipe = None
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try:
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gemma_pipe = pipeline("text-generation", model="google/gemma-3-1b-it", device=0)
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except Exception as e:
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st.error(f"Error loading Gemma model: {e}")
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gemma_pipe = None
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return score_pipe, gemma_pipe
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score_pipe, gemma_pipe = load_models()
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# Input: Query text for scoring and CSV file upload for candidate reviews
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query_input = st.text_area("Enter your query text for analysis (this does not need to be part of the CSV):")
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uploaded_file = st.file_uploader("Upload Reviews CSV File (must contain a 'reviewText' column)", type=["csv"])
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if score_pipe is None or gemma_pipe is None:
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st.error("Model loading failed. Please check your model names, token permissions, and GPU configuration.")
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else:
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candidate_docs = []
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if uploaded_file is not None:
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try:
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df = pd.read_csv(uploaded_file)
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if 'reviewText' not in df.columns:
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st.error("CSV must contain a 'reviewText' column.")
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else:
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candidate_docs = df['reviewText'].dropna().astype(str).tolist()
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except Exception as e:
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st.error(f"Error reading CSV file: {e}")
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if st.button("Generate Report"):
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# Reset timer state so that the timer always shows up
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st.session_state.timer_started = False
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st.session_state.timer_frozen = False
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# Display the timer every time
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html(timer(), height=50)
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if uploaded_file is None:
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st.error("Please upload a CSV file.")
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elif not candidate_docs:
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st.error("CSV must contain a 'reviewText' column.")
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elif not query_input.strip():
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st.error("Please enter a query text!")
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else:
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if not st.session_state.timer_started and not st.session_state.timer_frozen:
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st.session_state.timer_started = True
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html(timer(), height=50)
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status_text = st.empty()
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progress_bar = st.progress(0)
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try:
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# Stage 1: Score candidate documents using the provided query.
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status_text.markdown("**π Scoring candidate documents...**")
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progress_bar.progress(0)
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scored_docs = []
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for doc in candidate_docs:
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combined_text = f"Query: {query_input} Document: {doc}"
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result = score_pipe(combined_text)[0]
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scored_docs.append((doc, result["score"]))
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progress_bar.progress(50)
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# Stage 2: Generate Report using Gemma, including query and scored results.
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status_text.markdown("**π Generating report with Gemma...**")
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prompt = f"""
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Generate a detailed report based on the following analysis.
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Query:
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"{query_input}"
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Candidate Reviews with their scores:
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{scored_docs}
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Please provide a concise summary report explaining the insights derived from these scores.
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"""
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report = gemma_pipe(prompt, max_length=200)
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progress_bar.progress(100)
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status_text.success("**β
Generation complete!**")
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html("<script>localStorage.setItem('freezeTimer', 'true');</script>", height=0)
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st.session_state.timer_frozen = True
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st.write("**Scored Candidate Reviews:**", scored_docs)
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st.write("**Generated Report:**", report[0]['generated_text'])
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except Exception as e:
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html("<script>document.getElementById('timer').remove();</script>")
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status_text.error(f"**β Error:** {str(e)}")
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progress_bar.empty()
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