Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,45 +2,41 @@ import streamlit as st
 from transformers import pipeline
 from PIL import Image
 
-st.set_page_config(page_title="🤖 AI Toolbox", layout="centered")
-st.title("🤗 Hugging Face Streamlit App")
+ 
+st.title("Multimodal AI App 🤖")
+st.sidebar.header("🔧 Choose Task")
 
-# Sidebar for model choice
-task = st.sidebar.radio("Choose a Task", ["Text Generation", "Visual QA", "Text Summarization"])
+task = st.sidebar.selectbox("📂 Select task", ["🖼️ Visual Question Answering", "🌐 Translate to Urdu", "📖 Story Generator"])
 
-# ----------------- TEXT GENERATION -----------------
-if task == "Text Generation":
-    st.subheader("📝 Text Generation (GPT-2)")
-    prompt = st.text_area("Enter a prompt", "Once upon a time in a land far away,")
-    if st.button("Generate Text"):
-        with st.spinner("Generating..."):
-            generator = pipeline("text-generation", model="gpt2")
-            output = generator(prompt, max_length=100, num_return_sequences=1)
-            st.success("Generated Text:")
-            st.write(output[0]['generated_text'])
+if task == "🖼️ Visual Question Answering":
+    st.header("🖼️ Visual Question Answering")
+    uploaded_file = st.file_uploader("📤 Upload an image", type=["jpg", "png", "jpeg"])
+    question = st.text_input("❓ Ask a question about the image")
+    if uploaded_file and question:
+        image = Image.open(uploaded_file)
+        if st.button("🔍 Ask Question"):
+            with st.spinner('⏳ Loading VQA model...'):
+                vqa_pipe = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
+                result = vqa_pipe(image, question)
+            st.image(image, caption="🖼️ Uploaded Image")
+            st.success(f"✅ **Answer:** {result[0]['answer']}")
+
+elif task == "🌐 Translate to Urdu":
+    st.header("🌐 English to Urdu Translation")
+    input_text = st.text_area("✏️ Enter English text")
+    if st.button("🌍 Translate"):
+        with st.spinner('⏳ Loading Translation model...'):
+            translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")
+            translation = translator(input_text, src_lang="eng_Latn", tgt_lang="urd_Arab")
+        st.success(f"✅ **Urdu Translation:** {translation[0]['translation_text']}")
+
+elif task == "📖 Story Generator":
+    st.header("📝 Story Generator")
+    prompt = st.text_input("💡 Enter a prompt")
+    if st.button("✍️ Generate Story"):
+        with st.spinner('⏳ Loading Text Generation model...'):
+            text_gen_pipe = pipeline("text-generation", model="openai-community/gpt2")
+            result = text_gen_pipe(prompt, max_length=100, num_return_sequences=1)
+        st.success(f"✅ **Generated Text:** {result[0]['generated_text']}")
 
-# ----------------- VISUAL QUESTION ANSWERING -----------------
-elif task == "Visual QA":
-    st.subheader("🖼️ Visual Question Answering")
-    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-    question = st.text_input("Ask a question about the image", "What colors are used in this image?")
-    
-    if uploaded_image and question:
-        image = Image.open(uploaded_image)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-        if st.button("Get Answer"):
-            with st.spinner("Answering..."):
-                vqa = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
-                result = vqa(image, question)
-                st.success(f"Answer: {result[0]['answer']}")
 
-# ----------------- TEXT SUMMARIZATION -----------------
-elif task == "Text Summarization":
-    st.subheader("📚 Text Summarization")
-    input_text = st.text_area("Paste long text here", height=200)
-    if st.button("Summarize"):
-        with st.spinner("Summarizing..."):
-            summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
-            summary = summarizer(input_text, max_length=130, min_length=30, do_sample=False)
-            st.success("Summary:")
-            st.write(summary[0]['summary_text'])