Update app.py

app.py

@@ -1,49 +1,28 @@
 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
 
 # Constants
+REPO_ID = "Prajwalds1/seceye"
 KNOWN_FOLDER = "known_faces"
-ESP32_SERVER_URL = "https://esp32-upload-server.onrender.com"
 MODEL_NAME = "ArcFace"
 DETECTOR_BACKEND = "retinaface"
 
-# Ensure known folder exists
-os.makedirs(KNOWN_FOLDER, exist_ok=True)
-
-# Fetch Hugging Face token securely from secrets
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# Check if the token is available
-if not HF_TOKEN:
-    raise ValueError("Hugging Face token is missing")
-
-# Define your repository URL
-HF_REPO = "https://huggingface.co/Prajwalds1/seceye"  # Your repo URL
-UPLOAD_URL = f"{HF_REPO}/upload"
+# Hugging Face Token from Secrets
+HF_TOKEN = os.getenv("HF_TOKEN")
 
 # Streamlit setup
-st.set_page_config(page_title="Second Eye - Enhanced Recognition", layout="centered")
+st.set_page_config(page_title="Second Eye", layout="centered")
 st.sidebar.title("Navigation")
 page = st.sidebar.radio("Go to", ["Face Recognition", "Upload Known Face"])
 
-# Fetch 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)
@@ -60,7 +39,7 @@ def preprocess_image(image_path):
     cv2.imwrite(output_path, final_img)
     return output_path
 
-# Check if a face is present
+# Face detection
 def is_face_detected(image_path):
     try:
         faces = DeepFace.extract_faces(
@@ -72,14 +51,26 @@ def is_face_detected(image_path):
     except:
         return False
 
-# Match against known faces
+# Compare with known faces
 def compare_with_known_faces(unknown_img_path):
-    for filename in os.listdir(KNOWN_FOLDER):
-        known_img_path = os.path.join(KNOWN_FOLDER, filename)
+    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)
+
         try:
             result = DeepFace.verify(
                 img1_path=unknown_img_path,
-                img2_path=known_img_path,
+                img2_path="temp_face.jpg",
                 model_name=MODEL_NAME,
                 detector_backend=DETECTOR_BACKEND,
                 enforce_detection=False
@@ -90,60 +81,68 @@ def compare_with_known_faces(unknown_img_path):
             continue
     return None
 
-# Upload known face to Hugging Face repository
-def upload_image_to_hf(image_path):
-    headers = {
-        "Authorization": f"Bearer {HF_TOKEN}"
-    }
-    files = {
-        "file": open(image_path, "rb")  # Open the image file for uploading
-    }
-    response = requests.post(UPLOAD_URL, headers=headers, files=files)
-    if response.status_code == 200:
-        st.success("✅ Image uploaded successfully to Hugging Face!")
-    else:
-        st.error(f"❌ Image upload failed with status code {response.status_code}: {response.text}")
+# 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 Page
 if page == "Upload Known Face":
     st.title("Upload Known Face")
-    uploaded = st.file_uploader("Choose an image of known person", type=["jpg", "jpeg", "png"])
-    name = st.text_input("Enter name of the person")
+    uploaded = st.file_uploader("Choose image", type=["jpg", "jpeg", "png"])
+    name = st.text_input("Enter name")
     if uploaded and name:
-        image = Image.open(uploaded)
-        filename = os.path.join(KNOWN_FOLDER, f"{name}.jpg")
-        image.save(filename)
-        st.success(f"✅ Face saved as {name}")
-
-        # Also upload to Hugging Face repo
-        upload_image_to_hf(filename)
+        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)
+        if success:
+            st.success(f"✅ Image saved as {filename} to Hugging Face repo.")
+        else:
+            st.error("❌ Failed to upload image.")
 
 # Face Recognition Page
 elif page == "Face Recognition":
     st.title("Second Eye - Face Recognition")
     if st.button("Capture and Recognize Face"):
-        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")
-
-            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}")
+        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")
+
+                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")
                 else:
-                    st.error("❌ No match found")
-                    tts = gTTS("No match found")
+                    st.warning("😕 No face detected")
+                    tts = gTTS("No face detected")
+                tts.save("result.mp3")
+                st.audio("result.mp3", autoplay=True)
             else:
-                st.warning("😕 No face detected")
-                tts = gTTS("No face detected")
+                st.warning("Failed to fetch image from ESP32")
+        except:
+            st.error("Error fetching from ESP32")
 
-            tts.save("result.mp3")
-            st.audio("result.mp3", autoplay=True)
-        else:
-            st.warning("No image found on ESP32 server")