Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import easyocr
+from transformers import pipeline, AutoTokenizer, AutoModel
+
+# Load CLIP model
+@st.cache_resource
+def load_clip_model():
+    pipe = pipeline("feature-extraction", model="fxmarty/clip-vision-model-tiny")
+    tokenizer = AutoTokenizer.from_pretrained("fxmarty/clip-vision-model-tiny")
+    model = AutoModel.from_pretrained("fxmarty/clip-vision-model-tiny")
+    return pipe, tokenizer, model
+
+pipe, tokenizer, model = load_clip_model()
+
+# Initialize OCR
+@st.cache_resource
+def load_ocr():
+    return easyocr.Reader(['en'])
+
+reader = load_ocr()
+
+# Streamlit App
+st.title("🖼️ Multimodal AI Assistant")
+st.write("Upload an image and ask a question about it!")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Display Image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Extract text using OCR
+    with st.spinner("Extracting text from image..."):
+        extracted_text = reader.readtext(uploaded_file, detail=0)
+
+    st.write("### 📝 Extracted Text:", extracted_text)
+
+    # User asks a question
+    user_question = st.text_input("🤖 Ask a question about the image:")
+
+    if user_question:
+        with st.spinner("Analyzing image and question..."):
+            inputs = tokenizer(user_question, return_tensors="pt")
+            outputs = model(**inputs)
+        
+        st.write("### 🏆 AI Response:")
+        st.write("CLIP Model Processed the Input! (Further improvements coming soon)")