Update app.py

app.py

@@ -1,47 +1,46 @@
-import streamlit as st
-from transformers import pipeline
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
 from diffusers import StableDiffusionPipeline
 import torch
 
-# Hugging Face token from secrets
-HF_TOKEN = st.secrets["HF_TOKEN"]
-
-# Streamlit page settings
-st.set_page_config(page_title="Tamil to Image Generator", layout="centered")
-
-st.title("🧠 Tamil to Image Generator 🎨")
-st.markdown("Enter Tamil text → Translate to English → Generate a creative story → Create an AI Image")
-
-# Input Tamil text
-tamil_input = st.text_area("Enter Tamil text", placeholder="உலகின் அழகான கடற்கரை பற்றி சொல்...")
-
-if st.button("Generate Image"):
-    if not tamil_input.strip():
-        st.warning("Please enter some Tamil text.")
-    else:
-        with st.spinner("🔁 Translating Tamil to English..."):
-            translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en")
-            translated = translator(tamil_input, max_length=100)[0]['translation_text']
-
-        st.success("✅ Translation done")
-        st.markdown(f"**📝 Translated Text:** `{translated}`")
-
-        with st.spinner("🧠 Generating creative English text..."):
-            generator = pipeline("text-generation", model="gpt2")
-            prompt_text = f"Describe this beautifully: {translated}"
-            generated = generator(prompt_text, max_length=80, do_sample=True, top_k=50)[0]['generated_text']
-
-        st.success("✅ Text generation done")
-        st.markdown(f"**🎨 Creative Description:** `{generated}`")
-
-        with st.spinner("🖼️ Generating AI Image... (may take 20–30 seconds)"):
-            pipe = StableDiffusionPipeline.from_pretrained(
-                "runwayml/stable-diffusion-v1-5",
-                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-                use_auth_token=HF_TOKEN
-            ).to("cuda" if torch.cuda.is_available() else "cpu")
-
-            image = pipe(prompt=generated).images[0]
-
-        st.success("✅ Image generated!")
-        st.image(image, caption="🖼️ AI Generated Image", use_column_width=True)
+# 1. Tamil to English Translator
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en")
+
+# 2. English Text Generator (you can use GPT2 or any causal model)
+generator = pipeline("text-generation", model="gpt2")
+
+# 3. Image Generator using Stable Diffusion
+device = "cuda" if torch.cuda.is_available() else "cpu"
+image_pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32
+)
+image_pipe = image_pipe.to(device)
+
+# 👇 Combined function
+def generate_image_from_tamil(tamil_input):
+    # Step 1: Translate Tamil → English
+    translated = translator(tamil_input, max_length=100)[0]['translation_text']
+
+    # Step 2: Generate English sentence based on translated input
+    generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text']
+
+    # Step 3: Generate Image based on English text
+    image = image_pipe(generated).images[0]
+
+    return translated, generated, image
+
+# 🎨 Gradio UI
+iface = gr.Interface(
+    fn=generate_image_from_tamil,
+    inputs=gr.Textbox(lines=2, label="Enter Tamil Text"),
+    outputs=[
+        gr.Textbox(label="Translated English Text"),
+        gr.Textbox(label="Generated English Prompt"),
+        gr.Image(label="Generated Image")
+    ],
+    title="Tamil to Image Generator 🌅",
+    description="Translates Tamil → English, generates story → creates image using Stable Diffusion."
+)
+
+iface.launch()