Update app.py

app.py

@@ -1,24 +1,25 @@
-import gradio as gr
+import streamlit as st
 from transformers import pipeline
 
-# Load the model pipeline with trust_remote_code=True
-pipe = pipeline("image-text-to-text", model="deepseek-ai/deepseek-vl2-small", trust_remote_code=True)
+# Ensure the latest Transformers library is used
+st.write("Initializing model...")
 
-# Define the Gradio function
-def generate_response(message):
-    messages = [{"role": "user", "content": message}]
-    result = pipe(messages)
-    return result[0]["generated_text"] if isinstance(result, list) else result
+try:
+    pipe = pipeline("image-text-to-text", model="deepseek-ai/deepseek-vl2-small", trust_remote_code=True)
+except ValueError as e:
+    st.error(f"Model loading failed: {e}")
+    st.stop()
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(label="Enter your message"),
-    outputs=gr.Textbox(label="Model Response"),
-    title="DeepSeek-VL2 Chatbot",
-    description="Ask anything and get a response from the DeepSeek-VL2-Small model."
-)
+st.title("DeepSeek-VL2 Chatbot")
 
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()
+# User input
+user_input = st.text_input("Enter your message:", "")
+
+if st.button("Generate Response"):
+    if user_input:
+        messages = [{"role": "user", "content": user_input}]
+        result = pipe(messages)
+        response = result[0]["generated_text"] if isinstance(result, list) else result
+        st.text_area("Model Response:", response, height=200)
+    else:
+        st.warning("Please enter a message.")