Update app.py

app.py

@@ -1,30 +1,30 @@
-import torch
-import os
-from PIL import Image
-from transformers import pipeline, AutoModel
+import gradio as gr
+from transformers import pipeline
 
-# ✅ Load the model using pipeline
+# Load the model pipeline
 pipe = pipeline("image-text-to-text", model="deepseek-ai/deepseek-vl2-small", trust_remote_code=True)
 
-# ✅ Load model directly (alternative approach)
-model = AutoModel.from_pretrained("deepseek-ai/deepseek-vl2-small", trust_remote_code=True)
-
-# ✅ Function to process image and text
-def predict(image_path, text_prompt):
-    # Ensure correct path format for Windows/Linux
-    image_path = image_path.replace("\\", "/")  
-    image = Image.open(image_path).convert("RGB")
+def chatbot(user_input):
+    """
+    Function to process user input using the DeepSeek-VL2-Small model.
+    """
+    messages = [{"role": "user", "content": user_input}]
+    response = pipe(messages)
     
-    messages = [{"role": "user", "content": text_prompt}]
-    result = pipe(image, messages)
-    return result
+    if isinstance(response, list) and len(response) > 0:
+        return response[0]["generated_text"]  # Extract response text
+    else:
+        return "No response received."
+
+# Create a Gradio interface
+demo = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Textbox(lines=2, placeholder="Ask something..."),
+    outputs="text",
+    title="DeepSeek-VL2 Chatbot",
+    description="Ask questions and get AI-generated responses using DeepSeek-VL2-Small."
+)
 
-# ✅ Example usage
+# Launch the Gradio app
 if __name__ == "__main__":
-    # Replace this with the correct image path
-    test_image = "sample_img2.JPG"  # Ensure this image exists in the same folder
-    prompt = "Describe this image."
-    
-    # Run prediction
-    output = predict(test_image, prompt)
-    print("Generated Response:", output)
+    demo.launch()