| # import streamlit as st | |
| # import numpy as np | |
| # import cv2 | |
| # import tempfile | |
| # import os | |
| # from PIL import Image | |
| # # ---- Page Configuration ---- | |
| # st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide") | |
| # st.title("📰 Fake News & Deepfake Detection Tool") | |
| # st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI") | |
| # # ---- Fake News Detection Section ---- | |
| # st.subheader("📝 Fake News Detection") | |
| # news_input = st.text_area("Enter News Text:", "Type here...") | |
| # if st.button("Check News"): | |
| # st.write("🔍 Processing...") | |
| # st.success("✅ Result: This news is FAKE.") # Replace with ML Model | |
| # # ---- Deepfake Image Detection Section ---- | |
| # st.subheader("📸 Deepfake Image Detection") | |
| # uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"]) | |
| # def compress_image(image, quality=90, max_size=(300, 300)): # ✅ High clarity image | |
| # img = Image.open(image).convert("RGB") | |
| # img.thumbnail(max_size) | |
| # temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") | |
| # img.save(temp_file.name, "JPEG", quality=quality) | |
| # return temp_file.name | |
| # if uploaded_image is not None: | |
| # compressed_image_path = compress_image(uploaded_image) | |
| # st.image(compressed_image_path, caption="🖼️ Compressed & Clear Image", use_column_width=True) | |
| # if st.button("Analyze Image"): | |
| # st.write("🔍 Processing...") | |
| # st.error("⚠️ Result: This image is a Deepfake.") # Replace with model | |
| # # ---- Deepfake Video Detection Section ---- | |
| # st.subheader("🎥 Deepfake Video Detection") | |
| # uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"]) | |
| # def compress_video(video): | |
| # temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") | |
| # with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video: | |
| # temp_video.write(video.read()) | |
| # video_path = temp_video.name | |
| # cap = cv2.VideoCapture(video_path) | |
| # if not cap.isOpened(): | |
| # st.error("❌ Error: Unable to read video!") | |
| # return None | |
| # fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
| # # ✅ New Resolution (100x80) & 15 FPS | |
| # frame_width = 50 | |
| # frame_height = 80 | |
| # out = cv2.VideoWriter(temp_file.name, fourcc, 15.0, (frame_width, frame_height)) | |
| # while cap.isOpened(): | |
| # ret, frame = cap.read() | |
| # if not ret: | |
| # break | |
| # frame = cv2.resize(frame, (frame_width, frame_height)) | |
| # out.write(frame) | |
| # cap.release() | |
| # out.release() | |
| # return temp_file.name | |
| # if uploaded_video is not None: | |
| # st.video(uploaded_video) # ✅ فوراً ویڈیو اپ لوڈ ہونے کے بعد دکھائیں | |
| # compressed_video_path = compress_video(uploaded_video) | |
| # if compressed_video_path: | |
| # st.video(compressed_video_path) # ✅ کمپریسڈ ویڈیو بھی دکھائیں | |
| # if st.button("Analyze Video"): | |
| # st.write("🔍 Processing...") | |
| # st.warning("⚠️ Result: This video contains Deepfake elements.") # Replace with model | |
| # st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**") | |
| # import streamlit as st | |
| # import numpy as np | |
| # import cv2 | |
| # import tempfile | |
| # import os | |
| # from PIL import Image | |
| # import tensorflow as tf | |
| # from transformers import pipeline | |
| # from tensorflow.keras.applications import Xception, EfficientNetB7 | |
| # from tensorflow.keras.models import Model | |
| # from tensorflow.keras.layers import Dense, GlobalAveragePooling2D | |
| # from tensorflow.keras.preprocessing.image import load_img, img_to_array | |
| # # ---- Page Configuration ---- | |
| # st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide") | |
| # st.title("📰 Fake News & Deepfake Detection Tool") | |
| # st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI") | |
| # # Load Models | |
| # fake_news_detector = pipeline("text-classification", model="microsoft/deberta-v3-base") | |
| # # Load Deepfake Detection Models | |
| # base_model_image = Xception(weights="imagenet", include_top=False) | |
| # base_model_image.trainable = False # Freeze base layers | |
| # x = GlobalAveragePooling2D()(base_model_image.output) | |
| # x = Dense(1024, activation="relu")(x) | |
| # x = Dense(1, activation="sigmoid")(x) # Sigmoid for probability output | |
| # deepfake_image_model = Model(inputs=base_model_image.input, outputs=x) | |
| # base_model_video = EfficientNetB7(weights="imagenet", include_top=False) | |
| # base_model_video.trainable = False | |
| # x = GlobalAveragePooling2D()(base_model_video.output) | |
| # x = Dense(1024, activation="relu")(x) | |
| # x = Dense(1, activation="sigmoid")(x) | |
| # deepfake_video_model = Model(inputs=base_model_video.input, outputs=x) | |
| # # Function to Preprocess Image | |
| # def preprocess_image(image_path): | |
| # img = load_img(image_path, target_size=(299, 299)) # Xception expects 299x299 | |
| # img = img_to_array(img) | |
| # img = np.expand_dims(img, axis=0) | |
| # img /= 255.0 # Normalize pixel values | |
| # return img | |
| # # Function to Detect Deepfake Image | |
| # def detect_deepfake_image(image_path): | |
| # image = preprocess_image(image_path) | |
| # prediction = deepfake_image_model.predict(image)[0][0] | |
| # confidence = round(float(prediction), 2) | |
| # label = "FAKE" if confidence > 0.5 else "REAL" | |
| # return {"label": label, "score": confidence} | |
| # # ---- Fake News Detection Section ---- | |
| # st.subheader("📝 Fake News Detection") | |
| # news_input = st.text_area("Enter News Text:", placeholder="Type here...") | |
| # if st.button("Check News"): | |
| # st.write("🔍 Processing...") | |
| # prediction = fake_news_detector(news_input) | |
| # label = prediction[0]['label'] | |
| # confidence = prediction[0]['score'] | |
| # if label == "FAKE": | |
| # st.error(f"⚠️ Result: This news is FAKE. (Confidence: {confidence:.2f})") | |
| # else: | |
| # st.success(f"✅ Result: This news is REAL. (Confidence: {confidence:.2f})") | |
| # # ---- Deepfake Image Detection Section ---- | |
| # st.subheader("📸 Deepfake Image Detection") | |
| # uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"]) | |
| # if uploaded_image is not None: | |
| # temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") | |
| # img = Image.open(uploaded_image).convert("RGB") | |
| # img.save(temp_file.name, "JPEG") | |
| # st.image(temp_file.name, caption="🖼️ Uploaded Image", use_column_width=True) | |
| # if st.button("Analyze Image"): | |
| # st.write("🔍 Processing...") | |
| # result = detect_deepfake_image(temp_file.name) | |
| # if result["label"] == "FAKE": | |
| # st.error(f"⚠️ Result: This image is a Deepfake. (Confidence: {result['score']:.2f})") | |
| # else: | |
| # st.success(f"✅ Result: This image is Real. (Confidence: {1 - result['score']:.2f})") | |
| # # ---- Deepfake Video Detection Section ---- | |
| # st.subheader("🎥 Deepfake Video Detection") | |
| # uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"]) | |
| # def detect_deepfake_video(video_path): | |
| # cap = cv2.VideoCapture(video_path) | |
| # frame_scores = [] | |
| # while cap.isOpened(): | |
| # ret, frame = cap.read() | |
| # if not ret: | |
| # break | |
| # frame_path = "temp_frame.jpg" | |
| # cv2.imwrite(frame_path, frame) | |
| # result = detect_deepfake_image(frame_path) | |
| # frame_scores.append(result["score"]) | |
| # os.remove(frame_path) | |
| # cap.release() | |
| # avg_score = np.mean(frame_scores) | |
| # final_label = "FAKE" if avg_score > 0.5 else "REAL" | |
| # return {"label": final_label, "score": round(float(avg_score), 2)} | |
| # if uploaded_video is not None: | |
| # st.video(uploaded_video) | |
| # temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") | |
| # with open(temp_file.name, "wb") as f: | |
| # f.write(uploaded_video.read()) | |
| # if st.button("Analyze Video"): | |
| # st.write("🔍 Processing...") | |
| # result = detect_deepfake_video(temp_file.name) | |
| # if result["label"] == "FAKE": | |
| # st.warning(f"⚠️ Result: This video contains Deepfake elements. (Confidence: {result['score']:.2f})") | |
| # else: | |
| # st.success(f"✅ Result: This video is Real. (Confidence: {1 - result['score']:.2f})") | |
| # st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**") | |
| import streamlit as st | |
| import numpy as np | |
| from transformers import pipeline | |
| import requests | |
| # ---- Page Configuration ---- | |
| st.set_page_config(page_title="Fake News & Deepfake Detection", layout="wide") | |
| st.title("📰 Fake News & Deepfake Detection Tool") | |
| st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI") | |
| # Load Improved Fake News Detection Model | |
| fake_news_detector = pipeline("text-classification", model="roberta-base-openai-detector") | |
| # Fact-Checking API Function | |
| def fact_check_google(news_text): | |
| api_url = f'https://factchecktools.googleapis.com/v1alpha1/claims:search?query={news_text}&key=YOUR_GOOGLE_FACTCHECK_API_KEY' | |
| response = requests.get(api_url) | |
| if response.status_code == 200: | |
| data = response.json() | |
| if "claims" in data: | |
| return data["claims"] | |
| return None | |
| # ---- Fake News Detection Section ---- | |
| st.subheader("📝 Fake News Detection") | |
| news_input = st.text_area("Enter News Text:", placeholder="Type here...") | |
| if st.button("Check News"): | |
| st.write("🔍 Processing...") | |
| # Step 1: AI-Based Classification | |
| prediction = fake_news_detector(news_input) | |
| label = prediction[0]['label'] | |
| confidence = prediction[0]['score'] | |
| # Step 2: Fact Checking via API | |
| fact_check_result = fact_check_google(news_input) | |
| if label == "FAKE": | |
| st.error(f"⚠️ Result: This news is FAKE. (Confidence: {confidence:.2f})") | |
| else: | |
| st.success(f"✅ Result: This news is REAL. (Confidence: {confidence:.2f})") | |
| # Display Fact Check Results | |
| if fact_check_result: | |
| st.write("📜 Fact Check Results:") | |
| for claim in fact_check_result: | |
| st.write(f"🔹 {claim['text']} - *{claim['claimReview'][0]['textualRating']}*") | |
| else: | |
| st.warning("⚠️ No Fact-Check Data Available.") | |
| st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**") | |