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app.py

@@ -0,0 +1,44 @@
+
+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import torch
+
+st.set_page_config(page_title="AI Misinformation & Deepfake Detector", layout="wide")
+
+# Sidebar selection
+option = st.sidebar.selectbox("Choose Detection Type", ["Text Misinformation", "Image Deepfake"])
+
+if option == "Text Misinformation":
+    st.title("🧠 Text Misinformation Detection")
+    text_input = st.text_area("Enter text to analyze:", height=150)
+    if st.button("Analyze Text"):
+        if text_input.strip():
+            model_name = "mrm8488/bert-tiny-finetuned-fake-news-detection"
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            model = AutoModelForSequenceClassification.from_pretrained(model_name)
+            nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
+            result = nlp(text_input)[0]
+            st.success(f"Prediction: {result['label']} ({round(result['score']*100, 2)}%)")
+        else:
+            st.warning("Please enter some text.")
+
+elif option == "Image Deepfake":
+    st.title("🖼️ Image Deepfake Detection")
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        if st.button("Analyze Image"):
+            model_name = "prithivMLmods/Deep-Fake-Detector-v2-Model"
+            processor = ViTImageProcessor.from_pretrained(model_name)
+            model = ViTForImageClassification.from_pretrained(model_name)
+            inputs = processor(images=image, return_tensors="pt")
+            with torch.no_grad():
+                outputs = model(**inputs)
+                logits = outputs.logits
+                predicted_class = torch.argmax(logits, dim=1).item()
+                label = model.config.id2label[predicted_class]
+                st.success(f"Prediction: {label}")

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+streamlit
+transformers
+torch
+pillow