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| import streamlit as st | |
| from utils import extract_text, anonymize_text, score_synopsis | |
| from llama_cpp import Llama | |
| import os | |
| from huggingface_hub import snapshot_download | |
| from huggingface_hub import login | |
| st.set_page_config(page_title="Synopsis Scorer", layout="wide") | |
| # --- Access Control --- | |
| TOKEN = st.secrets.get("access_token") | |
| user_token = st.text_input("Enter Access Token to Continue", type="password") | |
| if user_token != TOKEN: | |
| st.warning("Please enter a valid access token.") | |
| st.stop() | |
| # --- Hugging Face Token Configuration --- | |
| hf_token = st.secrets.get("hf_token") if "hf_token" in st.secrets else os.environ.get("HF_TOKEN") | |
| if not hf_token and not os.path.exists("models/gemma-3-4b-it-q4_0.gguf"): | |
| st.warning("Hugging Face token not found. Please add it to your secrets or environment variables.") | |
| hf_token = st.text_input("Enter your Hugging Face token:", type="password") | |
| login(hf_token) | |
| print("Looking for model at:", os.path.abspath("gemma-3-4b-it-q4_0.gguf")) | |
| # Choose a directory to store the model | |
| model_dir = "./gemma-3-4b-it-qat-q4_0" | |
| # Download the GGUF model | |
| snapshot_download( | |
| repo_id="google/gemma-3-4b-it-qat-q4_0-gguf", | |
| local_dir=model_dir, | |
| local_dir_use_symlinks=False # Ensures real files are written, not symlinks | |
| ) | |
| # --- File Upload --- | |
| st.title("📘 Synopsis Scorer with Privacy Protection") | |
| article_file = st.file_uploader("Upload the Article (.pdf/.txt)", type=["pdf", "txt"]) | |
| synopsis_file = st.file_uploader("Upload the Synopsis (.txt)", type=["txt"]) | |
| if article_file and synopsis_file: | |
| with st.spinner("Reading files..."): | |
| article = extract_text(article_file) | |
| synopsis = extract_text(synopsis_file) | |
| st.subheader("Preview") | |
| st.text_area("Article", article[:1000] + "...", height=200) | |
| st.text_area("Synopsis", synopsis, height=150) | |
| if st.button("Evaluate"): | |
| with st.spinner("Scoring..."): | |
| scores = score_synopsis(article, synopsis) | |
| # Anonymization | |
| article_anon = anonymize_text(article) | |
| synopsis_anon = anonymize_text(synopsis) | |
| # Estimate n_ctx | |
| total_text = article_anon + synopsis_anon | |
| estimated_tokens = int(len(total_text)/3.5) | |
| n_ctx = estimated_tokens + 500 | |
| article_limit = 80000 # max_article_chars = 32,000 tokens×3.5 (approx_chars_per_token)≈112,000 characters; 112,000 - 32000(space for synopsis)= 80000 | |
| # LLM feedback | |
| try: | |
| llm = Llama( | |
| model_path="./gemma-3-4b-it-qat-q4_0/gemma-3-4b-it-q4_0.gguf", | |
| n_ctx=n_ctx, | |
| n_threads=2, | |
| n_batch=128 | |
| ) | |
| prompt = ( | |
| "You are an expert writing evaluator. The user has uploaded two text documents: " | |
| "1) a short synopsis, and 2) a longer article (source content). " | |
| "Without copying or storing the full content, analyze the synopsis and evaluate its quality in comparison to the article. " | |
| "Assess it on the basis of relevance, coverage, clarity, and coherence.\n\n" | |
| "Return:\n- A score out of 100\n- 2 to 3 lines of qualitative feedback\n\n" | |
| f"Here is the source article:\n{article_anon[:article_limit]}\n\nHere is the synopsis:\n{synopsis_anon}" | |
| ) | |
| result = llm.create_chat_completion(messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}]) | |
| feedback = result["choices"][0]["message"]["content"] | |
| except Exception as e: | |
| feedback = "LLM feedback not available: " + str(e) | |
| st.success("Evaluation Complete ✅") | |
| st.metric("Total Score", f"{scores['total']} / 100") | |
| st.progress(scores["total"] / 100) | |
| st.subheader("Score Breakdown") | |
| st.write(f"📘 Content Coverage: {scores['content_coverage']} / 50") | |
| st.write(f"🧠 Clarity: {scores['clarity']} / 25") | |
| st.write(f"🔗 Coherence: {scores['coherence']} / 25") | |
| st.subheader("LLM Feedback") | |
| st.write(feedback) | |