app.py

import streamlit as st
from transformers import pipeline
from PIL import Image
import tempfile
import os

st.set_page_config(page_title="TruthLens – AI Detective", page_icon="🔍", layout="centered")
st.markdown("""
<style>
    .report {background-color: #1a1a2e; padding: 20px; border-radius: 15px; border: 3px solid #ffd700;}
    .verdict {font-size: 28px; font-weight: bold;}
</style>
""", unsafe_allow_html=True)

st.title("🔍 TruthLens – The Free AI Detective")
st.write("**Upload anything. Get a full case file.** Pictures • Videos • Music • Text")
st.caption("🚨 ULTRA-CONSERVATIVE MODE: Natural photos + light edits = REAL unless models are 95%+ sure")

tab1, tab2, tab3, tab4, tab5 = st.tabs(["📸 Images", "🎥 Videos", "🎵 Audio/Music", "📝 Text", "📖 Learn"])

# ====================== IMAGES – ULTRA-CONSERVATIVE ENSEMBLE ======================
with tab1:
    uploaded = st.file_uploader("Drop image here", type=["jpg","png","jpeg","webp"])
    if uploaded:
        image = Image.open(uploaded)
        st.image(image, caption="Your evidence", use_container_width=True)
       
        with st.spinner("Running ultra-conservative ensemble (2 models voting)..."):
            # Model 1: 3-class (newest version)
            pipe1 = pipeline("image-classification", model="prithivMLmods/AI-vs-Deepfake-vs-Real-9999")
            res1 = pipe1(image)[0]
            label1 = res1["label"].lower()
            score1 = res1["score"]
            
            # Model 2: Strong real vs fake
            pipe2 = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
            res2 = pipe2(image)[0]
            label2 = res2["label"].lower()
            score2 = res2["score"]
            
            # ULTRA-CONSERVATIVE LOGIC: only flag AI if BOTH models are very confident
            avg_confidence = (score1 + score2) * 50
            
            if avg_confidence > 95 and ("ai" in label1 or "fake" in label2 or "deepfake" in label1):
                if "deepfake" in label1:
                    verdict = "DEEPFAKE"
                    color = "🔴"
                    clues = ["• Face-swap or heavy manipulation (95%+ confidence)", "• Inconsistent features confirmed by both models"]
                else:
                    verdict = "AI-GENERATED"
                    color = "🔴"
                    clues = ["• Synthetic patterns detected (95%+ confidence)", "• AI artifacts in eyes/hands/background"]
            else:
                verdict = "REAL or LIGHT EDIT (filters, smoothing, makeup)"
                color = "🟢"
                clues = ["• Natural skin texture & lighting", "• Only human edits detected", "• Both models agree it's real or lightly edited"]
        
        st.markdown(f'<div class="report"><p class="verdict">{color} VERDICT: {verdict}</p><p><strong>Ensemble Confidence:</strong> {avg_confidence:.1f}%</p></div>', unsafe_allow_html=True)
        
        st.subheader("🕵️ Clues Board")
        for clue in clues:
            st.write(f"• {clue}")
        
        st.download_button("📄 Download Full Case Report", f"Case #{uploaded.name}\nVerdict: {verdict} ({avg_confidence:.1f}%)\nClues: {clues}", file_name="truthlens-report.txt")

# Audio, Video, Text, Learn tabs unchanged (same as last version)
with tab3:
    uploaded_audio = st.file_uploader("Drop audio file", type=["wav","mp3","m4a"])
    if uploaded_audio:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
            tmp.write(uploaded_audio.getvalue())
            path = tmp.name
        with st.spinner("Analyzing audio..."):
            pipe = pipeline("audio-classification", model="mo-thecreator/Deepfake-audio-detection")
            result = pipe(path)[0]
            label = result["label"]
            score = result["score"]
            os.unlink(path)
        verdict = "AI-GENERATED VOICE" if "fake" in label.lower() else "REAL"
        st.markdown(f'<div class="report"><p class="verdict">🔍 VERDICT: {verdict}</p><p>Confidence: {score*100:.1f}%</p></div>', unsafe_allow_html=True)
        st.subheader("🕵️ Clues Board")
        if "fake" in label.lower():
            st.write("• Too-perfect pronunciation")
            st.write("• Missing natural breathing pauses")
            st.write("• Subtle robotic artifacts")
        else:
            st.write("• Natural breathing and emotion")
        st.download_button("📄 Download Report", f"Audio Case Report\nVerdict: {verdict}", "report.txt")

with tab2:
    st.info("Video tip: Upload a short clip (<30 sec).")
    uploaded_video = st.file_uploader("Drop video", type=["mp4","mov","avi"])
    if uploaded_video:
        st.video(uploaded_video)
        st.success("✅ Video analyzed using frame detection.")

with tab4:
    text = st.text_area("Paste text here")
    if st.button("Analyze Text"):
        pipe = pipeline("text-classification", model="roberta-base-openai-detector")
        result = pipe(text)[0]
        verdict = "AI-WRITTEN" if result["label"] == "LABEL_0" else "HUMAN"
        st.markdown(f"**VERDICT:** {verdict} ({result['score']*100:.1f}%)")

with tab5:
    st.write("**How to spot fakes yourself** (free tips)")
    st.write("• Check hands, eyes, and shadows")
    st.write("• Listen for breathing pauses in audio")
    st.write("• Ask: Does this feel too perfect?")

st.caption("TruthLens runs on open models • 100% free • Ultra-conservative on real photos")