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Dockerfile

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+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

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 ---
-title: Ui Test 002
-emoji: 🏆
-colorFrom: blue
+title: Deepfake Misinfo
+emoji: 🚀
+colorFrom: red
 colorTo: red
-sdk: gradio
-sdk_version: 5.46.1
-app_file: app.py
+sdk: streamlit
+app_file: streamlit_app.py
+tags:
+  - streamlit
+  - deepfake
+  - misinformation
+  - transformers
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Welcome to Streamlit!
+
+Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).

gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+streamlit
+transformers
+torch
+pillow

streamlit_app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Sidebar for module selection
+module = st.sidebar.selectbox("Choose Detection Type", ["Text Misinformation", "Image Deepfake"])
+
+# Load models directly from Hugging Face (no local saving)
+@st.cache_resource
+def load_text_model():
+    return pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
+
+@st.cache_resource
+def load_image_model():
+    return pipeline("image-classification", model="prithivMLmods/Deep-Fake-Detector-v2-Model")
+
+# Text Misinformation Detection
+if module == "Text Misinformation":
+    st.title("📰 Misinformation Detection")
+    user_input = st.text_area("Enter a news statement or claim:")
+    if st.button("Analyze"):
+        if user_input.strip():
+            model = load_text_model()
+            result = model(user_input)[0]
+            st.success(f"Prediction: {result['label']}")
+            st.write(f"Confidence: {result['score']:.2f}")
+        else:
+            st.warning("Please enter some text.")
+
+
+# Image Deepfake Detection
+elif module == "Image Deepfake":
+    st.title("🖼️ Deepfake Image Detection")
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        model = load_image_model()
+        result = model(image)[0]
+
+        # Map label_0 and label_1 to meaningful labels with icons and colors
+        label_map = {
+            "LABEL_0": ("Unauthentic", "❌", "red"),
+            "LABEL_1": ("Authentic", "✅", "green")
+        }
+        label, icon, color = label_map.get(result['label'].upper(), (result['label'], "❓", "gray"))
+
+        st.markdown(
+            f"<h4 style='color:{color};'>{icon} Prediction: {label}</h4>",
+            unsafe_allow_html=True
+        )
+        st.write(f"Confidence: {result['score']:.2f}")