Update app.py

app.py

@@ -1,65 +1,57 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-from PIL import Image
 import requests
+import os
+from PIL import Image
 
-# Load tokenizer and model for Urdu to English translation
-tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ur-en")
-model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ur-en")
+# Hugging Face model for Urdu to English translation (ensure it supports ur -> en)
+HF_API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-ur-en"
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")  # Set this in your environment
 
-# Load CLIPVision model for ASL fingerspelling recognition
-asl_model = pipeline("image-classification", model="aalof/clipvision-asl-fingerspelling")
+headers = {
+    "Authorization": f"Bearer {HF_API_TOKEN}"
+}
 
-# Function to translate Urdu to English
-def translate_urdu_to_english(urdu_text):
-    inputs = tokenizer(urdu_text, return_tensors="pt", padding=True)
-    outputs = model.generate(**inputs)
-    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translated_text
+# Sign images folder (e.g., a.jpg, b.jpg, hello.jpg, etc.)
+SIGN_IMAGE_FOLDER = "sign_images"
 
-# Function to classify ASL fingerspelling
-def classify_asl_image(image):
-    result = asl_model(image)
-    return result[0]['label']
+def translate_urdu_to_english(urdu_text):
+    payload = {"inputs": urdu_text}
+    response = requests.post(HF_API_URL, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()[0]['translation_text']
+    else:
+        st.error("Translation failed. Please try again.")
+        return ""
+
+def display_sign_language(text):
+    words = text.lower().split()
+    for word in words:
+        img_path_word = os.path.join(SIGN_IMAGE_FOLDER, f"{word}.jpg")
+        if os.path.exists(img_path_word):
+            st.image(img_path_word, caption=word)
+        else:
+            # Fallback to letter-by-letter spelling
+            for char in word:
+                if char.isalpha():
+                    img_path_char = os.path.join(SIGN_IMAGE_FOLDER, f"{char}.jpg")
+                    if os.path.exists(img_path_char):
+                        st.image(img_path_char, caption=char)
 
 # Streamlit UI
-st.title("🤟 Urdu to ASL Fingerspelling Translator")
+st.set_page_config(page_title="DeafTranslator", layout="centered")
+st.title("🤟 DeafTranslator")
+st.markdown("Translate **Urdu text** to **English** and see it in **sign language**.")
 
-# Urdu Text Input
-urdu_text = st.text_area("✍️ Enter Urdu text:")
+urdu_text = st.text_area("Enter Urdu Text", height=150)
 
-if st.button("🔁 Translate & Show ASL"):
+if st.button("Translate to Sign Language"):
     if not urdu_text.strip():
-        st.warning("Please enter some Urdu text first.")
+        st.warning("Please enter some Urdu text.")
     else:
-        with st.spinner("Translating Urdu to English..."):
-            translated_text = translate_urdu_to_english(urdu_text.strip())
-        
-        st.success("✅ Translated to English:")
-        st.markdown(f"**🔤 {translated_text}**")
-
-        st.markdown("**🤟 ASL Fingerspelling Representation (A–Z):**")
-        # Display fingerspelling for each character in translated text
-        for char in translated_text:
-            if char.isalpha():
-                image_url = f"https://raw.githubusercontent.com/madisonlang/sign-language-alphabet/main/images/{char.upper()}.jpg"
-                try:
-                    img = Image.open(requests.get(image_url, stream=True).raw)
-                    st.image(img, caption=char.upper(), width=80)
-                except Exception as e:
-                    st.warning(f"Missing image for: {char.upper()}")
-
-st.markdown("----")
-st.subheader("🖐️ Upload Hand Image for ASL Letter Prediction")
-
-# Image Upload for ASL Prediction
-uploaded_file = st.file_uploader("Upload an image of a single ASL fingerspelling gesture", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", width=300)
-
-    with st.spinner("Classifying ASL gesture..."):
-        asl_letter = classify_asl_image(image)
-
-    st.success(f"Predicted ASL Letter: **{asl_letter}**")
+        with st.spinner("Translating..."):
+            english_text = translate_urdu_to_english(urdu_text)
+        if english_text:
+            st.subheader("Translated English Text")
+            st.success(english_text)
+            st.subheader("Sign Language Representation")
+            display_sign_language(english_text)