Update app.py

app.py

@@ -1,92 +1,33 @@
 import streamlit as st
-import numpy as np
+import requests
+import os
 from PIL import Image
-from transformers import AutoImageProcessor
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
 import cv2
 import time
-import torch
-import requests
-import json
-import os
-
-# Groq API Configuration
-GROQ_API_KEY = os.getenv("HF_GROQ_API_KEY")  # Fetch key from Hugging Face secrets
-GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
-
-# Load processor
-processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
 
-# Placeholder sign labels
-sign_labels = {
-    0: "Hello",
-    1: "Thank You",
-    2: "Yes",
-    3: "No",
-    4: "Please"
-}
+# Load the improved Hugging Face model
+processor = AutoImageProcessor.from_pretrained("nateraw/gesture-classification")
+model = AutoModelForImageClassification.from_pretrained("nateraw/gesture-classification")
 
-# Function to classify sign and refine using Groq API
+# Function for sign classification
 def classify_sign(image):
     image = image.convert("RGB")
     inputs = processor(images=image, return_tensors="pt")
-    prediction = inputs['pixel_values'].argmax().item()
-    gesture = sign_labels.get(prediction % len(sign_labels), "Unknown Sign")
+    outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=-1).item()
+    labels = ["Hello", "Thank You", "Yes", "No", "Please"]  # Update with the actual model labels
+    return labels[prediction % len(labels)]
 
-    if GROQ_API_KEY:
-        response = requests.post(
-            GROQ_API_URL,
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {GROQ_API_KEY}"
-            },
-            json={
-                "model": "llama-3.3-70b-versatile",
-                "messages": [{"role": "user", "content": f"Refine this detected sign: {gesture}"}]
-            }
-        )
-
-        if response.status_code == 200:
-            return response.json()['choices'][0]['message']['content']
-
-    return gesture
-    
-# Function to generate sign video from text
-# Function to generate sign video from text
-def generate_sign_video(text):
-    if GROQ_API_KEY:
-        response = requests.post(
-            GROQ_API_URL,
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {GROQ_API_KEY}"
-            },
-            json={
-                "model": "llama-3.3-70b-versatile",
-                "messages": [{"role": "user", "content": f"Generate sign language video for: {text}"}]
-            }
-        )
-
-        if response.status_code == 200:
-            return "https://www.w3schools.com/html/mov_bbb.mp4"  # Open-source sample video
-
-    return "https://www.w3schools.com/html/mov_bbb.mp4"  # Fallback video URL
-
-
-    
 # Streamlit UI
-
 def main():
-    st.set_page_config(page_title="Sign Language Translator", layout="wide")
-    st.markdown("<h1 style='text-align: center; font-size: 40px; font-weight: bold; color: #4CAF50;'>🤟 Sign Language Translator</h1>", unsafe_allow_html=True)
+    st.title("Sign Language Translator")
 
-    tab1, tab2, tab3, tab4 = st.tabs([
-        "📸 **Image Load**",
-        "📷 **Take Picture**",
-        "🎥 **Live**",
-        "📝 **Text2Sign**"
-    ])
+    tab1, tab2, tab3, tab4 = st.tabs(["Image Load", "Take Picture", "Live", "Text2Sign"])
 
     with tab1:
+        st.subheader("📸 Image Load")
         uploaded_image = st.file_uploader("Upload an image of a hand gesture", type=["png", "jpg", "jpeg"])
         if uploaded_image:
             image = Image.open(uploaded_image)
@@ -95,6 +36,7 @@ def main():
             st.success(f"Detected Gesture: {gesture}")
 
     with tab2:
+        st.subheader("📷 Take Picture")
         camera_image = st.camera_input("Take a picture")
         if camera_image:
             image = Image.open(camera_image)
@@ -103,7 +45,8 @@ def main():
             st.success(f"Detected Gesture: {gesture}")
 
     with tab3:
-        if st.button("Enable Cam", key="enable_cam"):
+        st.subheader("📹 Live")
+        if st.button("Enable Cam"):
             cap = cv2.VideoCapture(0)
             stframe = st.image([])
 
@@ -113,37 +56,18 @@ def main():
                     break
                 image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                 gesture = classify_sign(image)
-                frame = cv2.putText(frame, f"Detected Gesture: {gesture}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+                frame = cv2.putText(frame, f"Gesture: {gesture}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                 stframe.image(frame, channels="BGR", use_container_width=True)
                 time.sleep(5)
             cap.release()
 
     with tab4:
+        st.subheader("📝 Text2Sign")
         text_input = st.text_area("Enter text to generate sign language (Max 200 characters)", max_chars=200)
         if st.button("Generate Sign"):
             if text_input:
-                video_url = generate_sign_video(text_input)
-                if video_url:
-                    st.video(video_url)
-                else:
-                    st.error("Failed to generate sign language video.")
+                st.video("https://www.w3schools.com/html/mov_bbb.mp4")  # Placeholder URL
             else:
                 st.warning("Please enter some text.")
 
-    with st.sidebar:
-        st.markdown("<h2 style='font-size:28px; font-weight: bold; color: #4CAF50;'>Menu</h2>", unsafe_allow_html=True)
-        if st.button("📖 About Us", use_container_width=True):
-            st.markdown("We are team SignAI. We leverage advanced AI and Groq technology to interpret sign language gestures, making communication more accessible.")
-        if st.button("📞 Contact Us", use_container_width=True):
-            st.markdown("""
-            Phone: +123 456 7890  
-            LinkedIn: [SignAI](#)  
-            Facebook: [SignAI](#)  
-            Email: info@signai.com  
-            Instagram: [@signai_official](#)
-            """)
-        if st.button("💬 Feedback", use_container_width=True):
-            st.text_area("We value your feedback! Please share your thoughts below:")
-
-if __name__ == "__main__":
-    main()
+if __name__ == "__main__":