FEAT: Update image upload functionality and integrate emotion analysis with DeepFace

.gitignore

@@ -1,8 +1,14 @@
 # Personal files
+PLAN.MD
+
+# Environment files
 .venv/*
 
+# Python cache files and directories
 *.ipynb_checkpoints/*
 *.ipynb_checkpoints
-
 *__pycache__/*
-*__pycache__
+*__pycache__
+
+# DeepFace model cache
+.deepface_cache/

requirements.txt

@@ -1,2 +1,8 @@
-streamlit
-moviepy==1.0.3
+streamlit==1.35.0
+Pillow==10.3.0
+numpy==1.26.4
+# Core AI library for this test
+deepface==0.0.94
+# Backend for DeepFace
+tensorflow-cpu==2.16.1
+tf-keras==2.16.0

src/streamlit_app.py

@@ -1,22 +1,62 @@
-import streamlit as st
-import tempfile
 import os
+import streamlit as st
+from PIL import Image
+import numpy as np
+from deepface import DeepFace
+import logging
+import cv2  # <-- THIS LINE WAS ADDED
+
+# Set home directories for model caching. This is good practice even for local testing.
+# On Windows, you might need to adjust this path if you encounter issues.
+os.environ['DEEPFACE_HOME'] = os.path.join(os.getcwd(), '.deepface_cache')
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="FER Test",
+    page_icon="😀",
+    layout="centered"
+)
+
+st.title("Step 1: Facial Emotion Recognition (FER) Test")
+st.write("Upload an image with a face to test the DeepFace library.")
 
-st.set_page_config(page_title="Video Upload Test")
-st.title("Video Upload and Playback Test")
+# --- Logger Configuration ---
+logging.basicConfig(level=logging.INFO)
+logging.getLogger('deepface').setLevel(logging.ERROR)
 
-# Create a file uploader widget
-uploaded_file = st.file_uploader("Choose a video file...", type=["mp4", "mov", "avi", "mkv"])
+
+# --- UI and Processing Logic ---
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # The uploader gives us an in-memory file buffer.
-    # We need to save it to a temporary file on the server's disk for st.video to use it.
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tfile:
-        tfile.write(uploaded_file.read())
-        temp_video_path = tfile.name
+    # Read the uploaded image file
+    pil_image = Image.open(uploaded_file)
+
+    # Convert the PIL image to a NumPy array
+    numpy_image = np.array(pil_image)
+    
+    # DeepFace expects the image in BGR format, so we convert from RGB
+    image_bgr = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR)
+
+
+    st.image(pil_image, caption="Image Uploaded", use_column_width=True)
+
+    with st.spinner("Analyzing image for emotion..."):
+        try:
+            # Analyze the image using DeepFace
+            analysis = DeepFace.analyze(
+                img_path=image_bgr,
+                actions=['emotion'],
+                enforce_detection=False, # Don't crash if no face is found
+                silent=True
+            )
 
-    # Display the video player
-    st.video(temp_video_path)
+            if isinstance(analysis, list) and len(analysis) > 0:
+                dominant_emotion = analysis[0]['dominant_emotion']
+                st.success(f"Dominant Emotion Detected: **{dominant_emotion.capitalize()}**")
+                st.write(analysis[0]['emotion']) # Display all emotion scores
+            else:
+                st.warning("No face detected in the image.")
 
-    # Clean up the temporary file after use
-    os.unlink(temp_video_path)
+        except Exception as e:
+            st.error(f"An error occurred during analysis: {e}")