Create app.py

app.py

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+# TruthLens – Lite (always-on CPU version)
+# Retrieval + Extractive Answer + Citations (no heavy generators)
+
+import re
+import numpy as np
+import pandas as pd
+import gradio as gr
+from sklearn.metrics.pairwise import cosine_similarity
+from sentence_transformers import SentenceTransformer
+
+# -------------------------------
+# Corpus (seed docs)
+# -------------------------------
+SAMPLE_DOCS = [
+    {"title": "IPCC on Climate Change",
+     "text": ("It is unequivocal that human influence has warmed the atmosphere, ocean and land. "
+              "Greenhouse gas emissions from human activities are responsible for approximately 1.1°C of warming since 1850–1900."),
+     "url": "https://example.org/ipcc"},
+    {"title": "Elections Security Myths",
+     "text": ("Independent audits and paper ballot backups reduce the risk of widespread election fraud. "
+              "No credible evidence supports claims of nationwide manipulation in recent elections."),
+     "url": "https://example.org/election-security"},
+    {"title": "WHO on Vaccines & Safety",
+     "text": ("Vaccines undergo rigorous testing in clinical trials and continuous safety monitoring. "
+              "Severe adverse reactions are rare and benefits outweigh risks."),
+     "url": "https://example.org/who-vaccines"},
+]
+
+# -------------------------------
+# Model (tiny, fast)
+# -------------------------------
+EMB_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+EMB = SentenceTransformer(EMB_MODEL)
+
+INDEX = {"emb": None, "texts": [], "titles": [], "urls": []}
+
+def _sent_split(text: str):
+    # lightweight sentence splitter
+    sents = re.split(r"(?<=[.!?])\s+", text.strip())
+    return [s.strip() for s in sents if s.strip()]
+
+def build_index(extra=None):
+    texts = [d["text"] for d in SAMPLE_DOCS]
+    titles = [d["title"] for d in SAMPLE_DOCS]
+    urls = [d["url"] for d in SAMPLE_DOCS]
+    # add user sources
+    if extra:
+        for i, t in enumerate(extra):
+            if t and str(t).strip():
+                texts.append(str(t).strip())
+                titles.append(f"User Source {i+1}")
+                urls.append("user://paste")
+    INDEX["texts"], INDEX["titles"], INDEX["urls"] = texts, titles, urls
+    INDEX["emb"] = EMB.encode(texts, normalize_embeddings=True, convert_to_numpy=True)
+
+def retrieve(query, k=3):
+    if INDEX["emb"] is None:
+        build_index()
+    q = EMB.encode([query], normalize_embeddings=True, convert_to_numpy=True)
+    sims = cosine_similarity(q, INDEX["emb"])[0]
+    top_idx = np.argsort(-sims)[:k]
+    return top_idx, sims
+
+def extractive_answer(query, doc_indices, max_sents=5):
+    # score sentences from selected docs against query; pick top unique sentences
+    q = EMB.encode([query], normalize_embeddings=True, convert_to_numpy=True)[0]
+    cand_sents = []
+    mapping = []  # (doc_i, sent_text)
+    for rank, di in enumerate(doc_indices):
+        sents = _sent_split(INDEX["texts"][di])[:10]
+        if not sents:
+            continue
+        emb = EMB.encode(sents, normalize_embeddings=True, convert_to_numpy=True)
+        sc = cosine_similarity([q], emb)[0]
+        for s, score in zip(sents, sc):
+            cand_sents.append((score, s, di, rank))
+            mapping.append((di, s))
+
+    # sort by score, then take diverse sentences (avoid near-duplicates)
+    cand_sents.sort(key=lambda x: -x[0])
+    picked = []
+    picked_embs = []
+    for score, s, di, _ in cand_sents:
+        if len(picked) >= max_sents: break
+        e = EMB.encode([s], normalize_embeddings=True, convert_to_numpy=True)[0]
+        if picked_embs:
+            simmax = float(np.max(cosine_similarity([e], np.vstack(picked_embs))[0]))
+            if simmax > 0.85:
+                continue
+        picked.append((s, di))
+        picked_embs.append(e)
+
+    if not picked:
+        return "I’m uncertain based on the provided sources.", []
+
+    # stitch into a paragraph with inline citations
+    parts = []
+    cites_used = set()
+    for s, di in picked:
+        tag = f"[{doc_indices.index(di)+1}]" if di in doc_indices else ""
+        parts.append(f"{s} {tag}")
+        cites_used.add(di)
+    paragraph = " ".join(parts)
+    citations = [f"[{i+1}] {INDEX['titles'][di]} – {INDEX['urls'][di]}"
+                 for i, di in enumerate(doc_indices)]
+    return paragraph.strip(), citations
+
+# -------------------------------
+# Pipeline
+# -------------------------------
+def run_pipeline(claim, s1, s2, s3):
+    build_index([s1, s2, s3])
+    idxs, sims = retrieve(claim, k=3)
+    answer, citations = extractive_answer(claim, list(idxs), max_sents=5)
+
+    # simple relevance table
+    table = pd.DataFrame({
+        "Source": [INDEX["titles"][i] for i in idxs],
+        "Relevance": [round(float(sims[i]), 3) for i in idxs]
+    })
+
+    # redacted = same as answer in Lite (no PII model)
+    redacted = answer
+    summary = "Mode: Lite (extractive). Answers are directly quoted/condensed from retrieved sources."
+
+    return summary, answer, "\n".join(citations), table, redacted
+
+# -------------------------------
+# UI
+# -------------------------------
+with gr.Blocks(title="TruthLens – Misinformation-Aware RAG (Lite)") as demo:
+    gr.Markdown(
+        "# 🧭 TruthLens – Lite\n"
+        "Reliable, CPU-friendly: retrieves sources and builds an **extractive answer** with citations."
+    )
+    with gr.Row():
+        with gr.Column():
+            claim = gr.Textbox(label="Claim or question", lines=2,
+                               placeholder="e.g., Did humans cause climate change?")
+            run_btn = gr.Button("Run TruthLens", variant="primary")
+        with gr.Column():
+            s1 = gr.Textbox(label="Optional source 1 (paste text)", lines=4)
+            s2 = gr.Textbox(label="Optional source 2 (paste text)", lines=4)
+            s3 = gr.Textbox(label="Optional source 3 (paste text)", lines=4)
+
+    summary = gr.Markdown()
+    answer = gr.Markdown(label="Answer")
+    cites = gr.Markdown(label="Citations")
+    table = gr.Dataframe(label="Top sources (similarity)")
+    redacted = gr.Textbox(label="PII-redacted answer", lines=3)
+
+    run_btn.click(fn=run_pipeline, inputs=[claim, s1, s2, s3],
+                  outputs=[summary, answer, cites, table, redacted])
+
+if __name__ == "__main__":
+    demo.launch()