Create app.py

app.py

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+# app.py
+import streamlit as st
+import torch
+from PIL import Image
+import io
+from transformers import AutoProcessor, AutoModelForCausalLM
+from peft import PeftModel
+
+# Page config
+st.set_page_config(
+    page_title="Deepfake Explainer",
+    page_icon="🔍",
+    layout="wide"
+)
+
+# App title and description
+st.title("Deepfake Image Analyzer")
+st.markdown("Upload an image to analyze it for possible deepfake manipulation")
+
+@st.cache_resource
+def load_model():
+    """Load model and processor (cached to avoid reloading)"""
+    # Load base model
+    base_model_id = "unsloth/llama-3.2-11b-vision-instruct"
+    processor = AutoProcessor.from_pretrained(base_model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model_id, 
+        device_map="auto", 
+        torch_dtype=torch.float16
+    )
+    
+    # Load adapter
+    adapter_id = "saakshigupta/deepfake-explainer-1"
+    model = PeftModel.from_pretrained(model, adapter_id)
+    
+    return model, processor
+
+# Function to fix cross-attention masks
+def fix_processor_outputs(inputs):
+    if 'cross_attention_mask' in inputs and 0 in inputs['cross_attention_mask'].shape:
+        batch_size, seq_len, _, num_tiles = inputs['cross_attention_mask'].shape
+        visual_features = 6404  # The exact dimension we fixed in training
+        new_mask = torch.ones((batch_size, seq_len, visual_features, num_tiles),
+                          device=inputs['cross_attention_mask'].device)
+        inputs['cross_attention_mask'] = new_mask
+        st.write("✅ Fixed cross-attention mask dimensions")
+    return inputs
+
+# Load model on first run
+with st.spinner("Loading model... this may take a minute."):
+    model, processor = load_model()
+    st.success("Model loaded successfully!")
+
+# Create sidebar with options
+with st.sidebar:
+    st.header("Options")
+    temperature = st.slider("Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.1,
+                           help="Higher values make output more random, lower values more deterministic")
+    max_length = st.slider("Maximum response length", min_value=100, max_value=1000, value=500, step=50)
+    
+    custom_prompt = st.text_area(
+        "Custom instruction (optional)",
+        value="Analyze this image and determine if it's a deepfake. Provide both technical and non-technical explanations.",
+        height=100
+    )
+    
+    st.markdown("### About")
+    st.markdown("This app uses a fine-tuned Llama 3.2 Vision model to detect and explain deepfakes.")
+    st.markdown("Model by [saakshigupta](https://huggingface.co/saakshigupta/deepfake-explainer-1)")
+
+# Main content area - file uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the image
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Analyze button
+    if st.button("Analyze Image"):
+        with st.spinner("Analyzing the image..."):
+            # Process the image
+            inputs = processor(text=custom_prompt, images=image, return_tensors="pt")
+            
+            # Fix cross-attention mask
+            inputs = fix_processor_outputs(inputs)
+            
+            # Move to device
+            inputs = {k: v.to(model.device) for k, v in inputs.items() if isinstance(v, torch.Tensor)}
+            
+            # Generate the analysis
+            with torch.no_grad():
+                output_ids = model.generate(
+                    **inputs,
+                    max_new_tokens=max_length,
+                    temperature=temperature,
+                    top_p=0.9
+                )
+            
+            # Decode the output
+            response = processor.decode(output_ids[0], skip_special_tokens=True)
+            
+            # Extract the actual response (removing the prompt)
+            if custom_prompt in response:
+                result = response.split(custom_prompt)[-1].strip()
+            else:
+                result = response
+        
+        # Display result in a nice format
+        st.success("Analysis complete!")
+        
+        # Show technical and non-technical explanations separately if they exist
+        if "Technical Explanation:" in result and "Non-Technical Explanation:" in result:
+            technical, non_technical = result.split("Non-Technical Explanation:")
+            technical = technical.replace("Technical Explanation:", "").strip()
+            
+            col1, col2 = st.columns(2)
+            with col1:
+                st.subheader("Technical Analysis")
+                st.write(technical)
+            
+            with col2:
+                st.subheader("Simple Explanation")
+                st.write(non_technical)
+        else:
+            st.subheader("Analysis Result")
+            st.write(result)
+else:
+    st.info("Please upload an image to begin analysis")