Update app.py

app.py

@@ -1,16 +1,20 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import pipeline
 from diffusers import StableDiffusionPipeline
 import torch
 
 @st.cache_resource
 def load_all_models():
-    # Load translation model
-    model_id = "ai4bharat/indictrans2-indic-en-dist-200M"
-    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_id, trust_remote_code=True)
+    # Load Tamil ➝ English translation model
+    translation_pipeline = pipeline(
+        "translation",
+        model="ai4bharat/indictrans2-indic-en-dist-200M",
+        tokenizer="ai4bharat/indictrans2-indic-en-dist-200M",
+        src_lang="ta", tgt_lang="en",
+        device=0 if torch.cuda.is_available() else -1,
+    )
 
-    # Load Stable Diffusion image generator
+    # Load image generation model
     img_pipe = StableDiffusionPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1",
         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
@@ -18,33 +22,29 @@ def load_all_models():
     )
     img_pipe = img_pipe.to("cuda" if torch.cuda.is_available() else "cpu")
 
-    return tokenizer, model, img_pipe
+    return translation_pipeline, img_pipe
 
 def main():
-    st.set_page_config(page_title="Tamil to English to Image", layout="centered")
-    st.title("📸 Tamil ➝ English ➝ AI Image Generator")
+    st.set_page_config(page_title="Tamil ➝ English ➝ Image", layout="centered")
+    st.title("🌐 Tamil to English to Image Generator")
 
     tamil_text = st.text_area("Enter Tamil text:", height=150)
 
     if st.button("Generate Image"):
         if not tamil_text.strip():
-            st.warning("Please enter some Tamil text.")
+            st.warning("Please enter Tamil text.")
             return
 
         with st.spinner("Loading models..."):
-            tokenizer, model, img_pipe = load_all_models()
+            translation_pipeline, img_pipe = load_all_models()
 
         with st.spinner("Translating Tamil to English..."):
-            # Prepare special format: "<2en> <tamil sentence>"
-            formatted_input = f"<2en> {tamil_text.strip()}"
-            inputs = tokenizer(formatted_input, return_tensors="pt")
-            output_ids = model.generate(**inputs)
-            translated = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
-            st.success(f"🔤 English Translation: `{translated}`")
+            translated = translation_pipeline(tamil_text)[0]["translation_text"]
+            st.success(f"🔤 Translated English: `{translated}`")
 
         with st.spinner("Generating image..."):
             image = img_pipe(prompt=translated).images[0]
-            st.image(image, caption="🖼️ AI-generated Image", use_column_width=True)
+            st.image(image, caption="🎨 AI-generated Image", use_column_width=True)
 
 if __name__ == "__main__":
     main()