Update app.py

app.py

@@ -1,66 +1,66 @@
-import streamlit as st
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import SystemMessage, HumanMessage
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-HF_TOKEN = os.environ.get('HF_TOKEN')
-os.environ['HF_TOKEN'] = HF_TOKEN
-
-llm = HuggingFaceEndpoint(
-    repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-    task="text-generation",
-    huggingfacehub_api_token=HF_TOKEN
-)
-llm = ChatHuggingFace(llm=llm)
-
-def getLLamaresponse(input_text, no_words, blog_style):
-    if not input_text or not no_words:
-        return "⚠️ Please enter a valid topic and word count."
-
-    try:
-        template = """
-        Write a blog for {blog_style} job profile on the topic "{input_text}" 
-        within {no_words} words.
-        """
-
-        prompt = ChatPromptTemplate.from_template(template)
-
-        messages = [
-            SystemMessage(content="You are a helpful blog writer."),
-            HumanMessage(content=prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
-        ]
-
-        response = llm.invoke(messages)  
-        return response.content if response else "⚠️ No response from the model."
-
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
-
-
-st.set_page_config(
-    page_title="Generate Blogs",
-    page_icon='🤖',
-    layout='centered',
-    initial_sidebar_state='collapsed'
-)
-
-st.header("Generate Blogs 🤖")
-
-
-input_text = st.text_input("Enter the Blog Topic")
-
-col1, col2 = st.columns([5, 5])
-with col1:
-    no_words = st.text_input('Number of Words')
-with col2:
-    blog_style = st.selectbox('Writing the blog for', 
-                              ('Researchers', 'Data Scientist', 'Common People'), 
-                              index=0)
-
-submit = st.button("Generate")
-if submit:
-    response = getLLamaresponse(input_text, no_words, blog_style)
-    st.write(response)
+import streamlit as st
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema import SystemMessage, HumanMessage
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+HF_TOKEN = os.environ.get('HF_TOKEN')
+os.environ['HF_TOKEN'] = HF_TOKEN
+
+llm = HuggingFaceEndpoint(
+    repo_id="tiiuae/falcon-7b-instruct",
+    task="text-generation",
+    huggingfacehub_api_token=HF_TOKEN
+)
+llm = ChatHuggingFace(llm=llm)
+
+def getLLamaresponse(input_text, no_words, blog_style):
+    if not input_text or not no_words:
+        return "⚠️ Please enter a valid topic and word count."
+
+    try:
+        template = """
+        Write a blog for {blog_style} job profile on the topic "{input_text}" 
+        within {no_words} words.
+        """
+
+        prompt = ChatPromptTemplate.from_template(template)
+
+        messages = [
+            SystemMessage(content="You are a helpful blog writer."),
+            HumanMessage(content=prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
+        ]
+
+        response = llm.invoke(messages)  
+        return response.content if response else "⚠️ No response from the model."
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+
+st.set_page_config(
+    page_title="Generate Blogs",
+    page_icon='🤖',
+    layout='centered',
+    initial_sidebar_state='collapsed'
+)
+
+st.header("Generate Blogs 🤖")
+
+
+input_text = st.text_input("Enter the Blog Topic")
+
+col1, col2 = st.columns([5, 5])
+with col1:
+    no_words = st.text_input('Number of Words')
+with col2:
+    blog_style = st.selectbox('Writing the blog for', 
+                              ('Researchers', 'Data Scientist', 'Common People'), 
+                              index=0)
+
+submit = st.button("Generate")
+if submit:
+    response = getLLamaresponse(input_text, no_words, blog_style)
+    st.write(response)