Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+model_id = "bczhou/tiny-llava-v1-hf"
+pipe = pipeline("image-to-text", model=model_id)
+
+def generate_text(prompt, image):
+    # Generate text description
+    prompt = f"USER: <image>\n{prompt}\nASSISTANT:"
+    text_description = pipe(images=image, prompt=prompt,generate_kwargs={"max_new_tokens": 200}))  # Batch for efficiency
+    return text_description[0]
+
+# Create Gradio interface
+inputs = [gr.Textbox(label="Input Text"), gr.Image(type="pil")]  # Input for uploading an image
+outputs = gr.Textbox(label="Generated Text")  # Output for displaying the text
+
+# Create the interface
+interface = gr.Interface(fn=generate_text, inputs=inputs, outputs=outputs, title="LLaVa Image-to-Text")
+
+# Launch the interface
+interface.launch()