Add the initial version

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+
+from haystack.nodes import TransformersImageToText
+from haystack.nodes import PromptNode, PromptTemplate
+from haystack import Pipeline
+
+image_to_text = TransformersImageToText(
+  model_name_or_path="nlpconnect/vit-gpt2-image-captioning",
+  use_gpu=True,
+  batch_size=16,
+  progress_bar=True
+)
+
+prompt_template = PromptTemplate(prompt="""
+You will receive a describing text of a photo.
+Try to come up with a nice Instagram caption that has a phrase rhyming with the text
+
+Describing text:{documents};
+Caption:
+""")
+
+# prompt_node = PromptNode(model_name_or_path="gpt-3.5-turbo", api_key=api_key, default_prompt_template=pt)
+
+prompt_node = PromptNode(model_name_or_path="google/flan-t5-large", default_prompt_template=prompt_template)
+# prompt_node = PromptNode(model_name_or_path="tiiuae/falcon-7b-instruct", api_key=hf_api_key, default_prompt_template=pt, model_kwargs={"trust_remote_code":True})
+
+captioning_pipeline = Pipeline()
+
+captioning_pipeline.add_node(component=image_to_text, name="image_to_text", inputs=["File"])
+captioning_pipeline.add_node(component=prompt_node, name="prompt_node", inputs=["image_to_text"])
+
+def generate_caption(image_file_paths):
+    print(image_file_paths)
+    # documents = image_to_text.generate_captions(image_file_paths=[image_file_paths])
+    # print(documents[0].content)
+    caption = captioning_pipeline.run(file_paths=image_file_paths)
+    return caption["results"][0]
+
+with gr.Blocks(theme="soft") as demo:
+    image = gr.Image(type="filepath")
+    submit_btn = gr.Button("✨ Captionate ✨")
+    caption = gr.Textbox(label="Caption")
+    submit_btn.click(fn=generate_caption, inputs=[image], outputs=[caption])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+farm-haystack==1.19.0
+gradio==3.39.0