update the app, remove the failing model

app.py

@@ -1,8 +1,9 @@
 import os
 import gradio as gr
 
-from haystack.components.generators import HuggingFaceAPIGenerator
-from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
+from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
+from haystack.dataclasses import ChatMessage
 from haystack import Pipeline
 from haystack.utils import Secret
 from image_captioner import ImageCaptioner
@@ -17,25 +18,29 @@ description = """
 
 It uses [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) model for image-to-text caption generation task.
 
-For Instagrammable captions, `mistralai/Mistral-7B-Instruct-v0.1` performs best, but try different models to see how they react to the same prompt.
+For Instagrammable captions, try different text-to-text models to see how they react to the same prompt.
 
-Built by [Bilge Yucel](https://twitter.com/bilgeycl) using [Haystack 2.0](https://github.com/deepset-ai/haystack) 💙
+Built by [Bilge Yucel](https://twitter.com/bilgeycl) using [Haystack](https://github.com/deepset-ai/haystack) 💙
 """
 
-prompt_template = """
+prompt_template =[ChatMessage.from_user("""
 You will receive a descriptive text of a photo.
 Try to generate a nice Instagram caption with a phrase rhyming with the text. Include emojis in the caption.
-                                                                                                                                   
+Just return one option without alternatives. Don't use hashtags.           
+                                                                                 
 Descriptive text: {{caption}};
 Instagram Caption:
-"""
+""")]
 
 hf_api_key = os.environ["HF_API_KEY"]
 
 def generate_caption(image_file_path, model_name):
     image_to_text = ImageCaptioner(model_name="Salesforce/blip-image-captioning-base")
-    prompt_builder = PromptBuilder(template=prompt_template)
-    generator = HuggingFaceAPIGenerator(api_type="serverless_inference_api", api_params={"model": model_name}, token=Secret.from_token(hf_api_key), generation_kwargs={"max_new_tokens":100})
+    prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
+    generator = HuggingFaceAPIChatGenerator(
+        api_type="serverless_inference_api", 
+        api_params={"model": model_name}, 
+        token=Secret.from_token(hf_api_key))
     
     captioning_pipeline = Pipeline()
     captioning_pipeline.add_component("image_to_text", image_to_text)
@@ -46,7 +51,7 @@ def generate_caption(image_file_path, model_name):
     captioning_pipeline.connect("prompt_builder", "generator")
 
     result = captioning_pipeline.run({"image_to_text":{"image_file_path":image_file_path}})
-    return result["generator"]["replies"][0]
+    return result["generator"]["replies"][0].text
 
 with gr.Blocks(theme="soft") as demo:
     gr.Markdown(value=description)
@@ -54,8 +59,8 @@ with gr.Blocks(theme="soft") as demo:
         image = gr.Image(type="filepath")
         with gr.Column():
             model_name = gr.Dropdown(
-                ["meta-llama/Llama-3.3-70B-Instruct","mistralai/Mistral-7B-Instruct-v0.1","OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "tiiuae/falcon-7b-instruct", "tiiuae/falcon-7b", "HuggingFaceH4/starchat-beta", "bigscience/bloom", "google/flan-t5-xxl"], 
-                value="mistralai/Mistral-7B-Instruct-v0.1", 
+                ["deepseek-ai/DeepSeek-V3.1-Terminus", "meta-llama/Llama-3.3-70B-Instruct", "openai/gpt-oss-20b", "Qwen/Qwen3-4B-Instruct-2507"], 
+                value="deepseek-ai/DeepSeek-V3.1-Terminus", 
                 label="Choose your model!"
                 )
             gr.Examples(["./whale.png", "./rainbow.jpeg", "./selfie.png"], inputs=image, label="Click on any example")