Update app.py

app.py

@@ -3,6 +3,7 @@ from pathlib import Path
 import streamlit as st
 from transformers import pipeline
 from dotenv import load_dotenv
+from langchain import PromptTemplate, HuggingFaceHub, LLMChain
 
 if Path(".env").is_file():
     load_dotenv(".env")
@@ -11,8 +12,29 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 
 def img2Text(url):
     image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
-    text = image_to_text(url)[0]["generated_text"]
+    text = image_to_text(url)
+    st.subheader("Caption :")
     st.subheader(text)
     print(text)
     return text
-img2Text("photo.jpg")
+img2Text("photo.png")
+
+#llm
+def generate_story(scenario):
+    template = """
+    You are a story teller;
+    You can generate a short story based on a simple narrative, the story should be no momre than 20 words;
+    CONTEXT: {scenario}
+    STORY:
+    """
+
+    prompt = PromptTemplate(template=template,input_variables=["scenario"])
+    llm_chain = LLMChain(prompt=prompt, 
+                     llm=HuggingFaceHub(repo_id="google/flan-t5-xl", 
+                                        model_kwargs={"temperature":0, 
+                                                      "max_length":64}))
+    story =llm_chain.run(scenario)
+    st.subheader("Story :")
+    st.subheader(story)
+    print(story)
+    return story