Update app.py

app.py

@@ -1,28 +1,44 @@
+# app.py
 import streamlit as st
+from deepface import DeepFace
 import requests
 from PIL import Image
-from io import BytesIO
 from gtts import gTTS
 import os
-from datetime import datetime
-from deepface import DeepFace
 import cv2
 import numpy as np
+from datetime import datetime
+from io import BytesIO
+from huggingface_hub import HfApi, HfFolder, Repository, upload_file
 
 # Constants
 REPO_ID = "Prajwalds1/seceye"
 KNOWN_FOLDER = "known_faces"
 MODEL_NAME = "ArcFace"
 DETECTOR_BACKEND = "retinaface"
-
-# Hugging Face Token from Secrets
-HF_TOKEN = os.getenv("HF_TOKEN")
+ESP32_SERVER_URL = "https://esp32-upload-server.onrender.com"
+HF_TOKEN = os.environ.get("HF_TOKEN")
 
 # Streamlit setup
-st.set_page_config(page_title="Second Eye", layout="centered")
+st.set_page_config(page_title="Second Eye - Enhanced Recognition", layout="centered")
 st.sidebar.title("Navigation")
 page = st.sidebar.radio("Go to", ["Face Recognition", "Upload Known Face"])
 
+# Authenticate with Hugging Face
+api = HfApi()
+
+# Fetch latest image from ESP32
+@st.cache_data(show_spinner=False)
+def get_latest_image():
+    try:
+        r = requests.get(f"{ESP32_SERVER_URL}/latest")
+        if r.status_code != 200:
+            return None
+        filename = r.json()["filename"]
+        return f"{ESP32_SERVER_URL}/uploads/{filename}"
+    except:
+        return None
+
 # Enhance image quality
 def preprocess_image(image_path):
     img = cv2.imread(image_path)
@@ -39,7 +55,7 @@ def preprocess_image(image_path):
     cv2.imwrite(output_path, final_img)
     return output_path
 
-# Face detection
+# Check if a face is present
 def is_face_detected(image_path):
     try:
         faces = DeepFace.extract_faces(
@@ -51,98 +67,97 @@ def is_face_detected(image_path):
     except:
         return False
 
-# Compare with known faces
+# Match against known faces
 def compare_with_known_faces(unknown_img_path):
-    response = requests.get(
-        f"https://huggingface.co/api/datasets/{REPO_ID}/tree/main/{KNOWN_FOLDER}",
-        headers={"Authorization": f"Bearer {HF_TOKEN}"}
-    )
-    files = response.json()
-
-    for file in files:
-        filename = file["path"].split("/")[-1]
-        file_url = f"https://huggingface.co/datasets/{REPO_ID}/resolve/main/{file['path']}"
-
-        response = requests.get(file_url)
-        with open("temp_face.jpg", "wb") as f:
-            f.write(response.content)
-
+    # Get list of image URLs from repo
+    files = api.list_repo_files(REPO_ID, repo_type="dataset")
+    face_files = [f for f in files if f.startswith(KNOWN_FOLDER)]
+    for file in face_files:
         try:
+            url = f"https://huggingface.co/datasets/{REPO_ID}/resolve/main/{file}"
+            r = requests.get(url)
+            temp_path = f"temp_face.jpg"
+            with open(temp_path, "wb") as f:
+                f.write(r.content)
             result = DeepFace.verify(
                 img1_path=unknown_img_path,
-                img2_path="temp_face.jpg",
+                img2_path=temp_path,
                 model_name=MODEL_NAME,
                 detector_backend=DETECTOR_BACKEND,
                 enforce_detection=False
             )
             if result["verified"]:
-                return filename.split('.')[0]
+                return os.path.splitext(os.path.basename(file))[0]
         except:
             continue
     return None
 
-# Upload image to Hugging Face repo
-def upload_to_hf_repo(image_bytes, filename):
-    api_url = f"https://huggingface.co/api/repos/{REPO_ID}/upload/main/{KNOWN_FOLDER}/{filename}"
-    response = requests.put(
-        api_url,
-        headers={
-            "Authorization": f"Bearer {HF_TOKEN}",
-            "Content-Type": "application/octet-stream"
-        },
-        data=image_bytes
-    )
-    return response.status_code == 200
+# Upload known face to Hugging Face repo
+def upload_known_face_to_hf(image_data, filename):
+    try:
+        image_bytes = BytesIO()
+        image_data.save(image_bytes, format="JPEG")
+        image_bytes.seek(0)
+        upload_path = f"{KNOWN_FOLDER}/{filename}"
+        upload_file(
+            path_or_fileobj=image_bytes,
+            path_in_repo=upload_path,
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+        return True
+    except Exception as e:
+        print("Upload failed:", e)
+        return False
 
-# Upload Known Face Page
+# Upload Known Face page
 if page == "Upload Known Face":
     st.title("Upload Known Face")
-    uploaded = st.file_uploader("Choose image", type=["jpg", "jpeg", "png"])
-    name = st.text_input("Enter name")
+    uploaded = st.file_uploader("Choose an image of a known person", type=["jpg", "jpeg", "png"])
+    name = st.text_input("Enter name of the person")
     if uploaded and name:
-        image = Image.open(uploaded).convert("RGB")
-        img_byte_arr = BytesIO()
-        image.save(img_byte_arr, format='JPEG')
-        img_bytes = img_byte_arr.getvalue()
-        filename = f"{name}.jpg"
-        success = upload_to_hf_repo(img_bytes, filename)
+        image = Image.open(uploaded)
+        success = upload_known_face_to_hf(image, f"{name}.jpg")
         if success:
-            st.success(f"✅ Image saved as {filename} to Hugging Face repo.")
+            st.success(f"✅ Image successfully uploaded as {name}.jpg")
         else:
-            st.error("❌ Failed to upload image.")
+            st.error("❌ Image upload failed")
+
+    # Display current known faces
+    with st.expander("View Uploaded Known Faces"):
+        files = api.list_repo_files(REPO_ID, repo_type="dataset")
+        face_files = [f for f in files if f.startswith(KNOWN_FOLDER)]
+        for file in face_files:
+            img_url = f"https://huggingface.co/datasets/{REPO_ID}/resolve/main/{file}"
+            st.image(img_url, caption=file.split("/")[-1], width=150)
 
-# Face Recognition Page
+# Face Recognition page
 elif page == "Face Recognition":
     st.title("Second Eye - Face Recognition")
     if st.button("Capture and Recognize Face"):
-        ESP32_URL = "https://esp32-upload-server.onrender.com/latest"
-        try:
-            res = requests.get(ESP32_URL)
-            filename = res.json()["filename"]
-            img_url = f"https://esp32-upload-server.onrender.com/uploads/{filename}"
-            image_response = requests.get(img_url)
-            if image_response.status_code == 200:
-                with open("latest.jpg", "wb") as f:
-                    f.write(image_response.content)
-                st.image("latest.jpg", caption="Captured Image")
+        image_url = get_latest_image()
+        if image_url:
+            st.image(image_url, caption="Captured Image", use_container_width=True)
+            response = requests.get(image_url)
+            with open("latest.jpg", "wb") as f:
+                f.write(response.content)
 
-                processed_img_path = preprocess_image("latest.jpg")
+            processed_img_path = preprocess_image("latest.jpg")
 
-                if is_face_detected(processed_img_path):
-                    match = compare_with_known_faces(processed_img_path)
-                    if match:
-                        st.success(f"✅ Match found: {match}")
-                        tts = gTTS(f"Match found: {match}")
-                    else:
-                        st.error("❌ No match found")
-                        tts = gTTS("No match found")
+            if is_face_detected(processed_img_path):
+                match = compare_with_known_faces(processed_img_path)
+                if match:
+                    st.success(f"✅ Match found: {match}")
+                    tts = gTTS(f"Match found: {match}")
                 else:
-                    st.warning("😕 No face detected")
-                    tts = gTTS("No face detected")
-                tts.save("result.mp3")
-                st.audio("result.mp3", autoplay=True)
+                    st.error("❌ No match found")
+                    tts = gTTS("No match found")
             else:
-                st.warning("Failed to fetch image from ESP32")
-        except:
-            st.error("Error fetching from ESP32")
+                st.warning("😕 No face detected")
+                tts = gTTS("No face detected")
 
+            tts.save("result.mp3")
+            st.audio("result.mp3", autoplay=True)
+        else:
+            st.warning("No image found on ESP32 server")