Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,46 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+from transformers import pipeline
+from PIL import Image
+
+st.set_page_config(page_title="🤖 AI Toolbox", layout="centered")
+st.title("🤗 Hugging Face Streamlit App")
+
+# Sidebar for model choice
+task = st.sidebar.radio("Choose a Task", ["Text Generation", "Visual QA", "Text Summarization"])
+
+# ----------------- 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="openai-community/gpt2")
+            output = generator(prompt, max_length=100, num_return_sequences=1)
+            st.success("Generated Text:")
+            st.write(output[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'])