Update app.py

app.py

@@ -1,6 +1,6 @@
 from PIL import Image
 from transformers import BlipProcessor, BlipForConditionalGeneration
-from langchain import LLMChain, PromptTemplate
+from langchain import HuggingFaceHub, LLMChain, PromptTemplate
 import gradio as gr
 import numpy as np
 import requests
@@ -20,16 +20,16 @@ def generate_caption_from_image(image_path):
 
 def generate_story_from_caption(caption):
     # Generate story based on caption
-    llm = HuggingFaceHub(huggingfacehub_api_token = os.getenv('HUGGING_FACE')}"},
-                        repo_id = "tiiuae/falcon-7b-instruct",
-                        verbose = False,
-                        model_kwargs = {"temperature":0.2, "max_new_tokens": 4000})
-    template="""You are a story teller.
-    You get a scenario as an input text, and generates a short story out of it.
+    llm = HuggingFaceModel(api_token=os.getenv('HUGGING_FACE_API_TOKEN'),
+                            repo_id="tiiuae/falcon-7b-instruct",
+                            verbose=False,
+                            model_kwargs={"temperature": 0.2, "max_new_tokens": 4000})
+    template = """You are a story teller.
+    You get a scenario as an input text, and generate a short story out of it.
     Context: {scenario}
     Story:"""
     prompt = PromptTemplate(template=template, input_variables=["scenario"])
-    #Let's create our LLM chain now
+    # Let's create our LLM chain now
     chain = LLMChain(prompt=prompt, llm=llm)
     story = chain.run(caption)
     start_index = story.find("Story:") + len("Story:")