Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,36 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import torch
+
+# Configuration de la page
+st.set_page_config(page_title="OCR Manuscrit avec TrOCR", layout="centered")
+
+st.title("✍️ OCR de texte manuscrit avec TrOCR")
+st.write("Chargez une image contenant du texte manuscrit pour en extraire le contenu.")
+
+# Chargement du modèle TrOCR pour manuscrit
+@st.cache_resource
+def load_model():
+    processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+    model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+    return processor, model
+
+processor, model = load_model()
+
+# Upload de l'image
+uploaded_file = st.file_uploader("📤 Charger une image manuscrite (format .png ou .jpg)", type=["png", "jpg", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="🖼️ Image chargée", use_column_width=True)
+
+    if st.button("🔍 Lancer la reconnaissance"):
+        with st.spinner("Reconnaissance en cours..."):
+            pixel_values = processor(images=image, return_tensors="pt").pixel_values
+            generated_ids = model.generate(pixel_values)
+            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+        st.success("✅ Texte reconnu :")
+        st.text_area("📝 Résultat OCR", generated_text, height=150)
 
-"""
-# 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])),
-    ))