Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import pickle
+import requests
+import io
+
+# ---------------------------
+# PAGE CONFIG
+# ---------------------------
+st.set_page_config(page_title="TruthGuard AI", layout="wide")
+
+st.title("🛡️ TruthGuard AI")
+st.subheader("Multi-Modal Fake News & AI Image Detection")
+
+# ---------------------------
+# LOAD MODELS
+# ---------------------------
+
+# Fake News Model (load from Hugging Face)
+@st.cache_resource
+def load_text_model():
+    url = "https://huggingface.co/Maheentouqeer1/truthguard-fake-news-detector/resolve/main/fake_news_model.pkl"
+    vectorizer_url = "https://huggingface.co/Maheentouqeer1/truthguard-fake-news-detector/resolve/main/tfidf_vectorizer.pkl"
+
+    model_file = requests.get(url).content
+    vectorizer_file = requests.get(vectorizer_url).content
+
+    model = pickle.loads(model_file)
+    vectorizer = pickle.loads(vectorizer_file)
+
+    return model, vectorizer
+
+# Image Model
+@st.cache_resource
+def load_image_model():
+    url = "https://huggingface.co/syeda-Rija20/image-detector/resolve/main/image_detector_finetuned.h5"
+    model_file = requests.get(url).content
+
+    with open("temp_model.h5", "wb") as f:
+        f.write(model_file)
+
+    model = tf.keras.models.load_model("temp_model.h5")
+    return model
+
+
+text_model, vectorizer = load_text_model()
+image_model = load_image_model()
+
+# ---------------------------
+# TEXT CLEANING FUNCTION
+# ---------------------------
+import re
+import string
+
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r'http\S+', '', text)
+    text = text.translate(str.maketrans('', '', string.punctuation))
+    text = re.sub(r'\d+', '', text)
+    return text
+
+# ---------------------------
+# TABS UI
+# ---------------------------
+tab1, tab2 = st.tabs(["📰 Fake News Detection", "🖼️ AI Image Detection"])
+
+# ===========================
+# TAB 1 → FAKE NEWS
+# ===========================
+with tab1:
+    st.header("Fake News Detector")
+
+    user_input = st.text_area("Enter news text:")
+
+    if st.button("Analyze News"):
+        if user_input.strip() == "":
+            st.warning("Please enter some text")
+        else:
+            clean = clean_text(user_input)
+            vector = vectorizer.transform([clean])
+
+            prediction = text_model.predict(vector)[0]
+            prob = text_model.predict_proba(vector)[0]
+
+            fake_prob = prob[0] * 100
+            real_prob = prob[1] * 100
+
+            if prediction == 0:
+                st.error(f"⚠️ FAKE NEWS ({fake_prob:.2f}%)")
+            else:
+                st.success(f"✅ REAL NEWS ({real_prob:.2f}%)")
+
+            st.write("### Confidence Scores")
+            st.write(f"Fake: {fake_prob:.2f}%")
+            st.write(f"Real: {real_prob:.2f}%")
+
+# ===========================
+# TAB 2 → IMAGE DETECTOR
+# ===========================
+with tab2:
+    st.header("AI Image Detector")
+
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "png"])
+
+    if uploaded_file is not None:
+        img = image.load_img(uploaded_file, target_size=(224, 224))
+        img_array = image.img_to_array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        prediction = image_model.predict(img_array)
+
+        confidence = float(prediction[0][0]) * 100
+
+        if prediction[0][0] > 0.5:
+            st.error(f"⚠️ AI GENERATED IMAGE ({confidence:.2f}%)")
+        else:
+            st.success(f"✅ REAL IMAGE ({100-confidence:.2f}%)")
+
+        st.write("### Confidence Score")
+        st.write(f"{confidence:.2f}%")