Update app.py

app.py

@@ -1,42 +1,58 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-from dotenv import load_dotenv
 import os
+from dotenv import load_dotenv
+import gradio as gr
+from langchain import PromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint
 
 # Load environment variables
 load_dotenv()
 
-# Load the model and tokenizer
-#tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
-#model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
-
-tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-rw-1b", trust_remote_code=True)
-
-# Function to generate blog content
-def generate_blog(topic, keywords):
-    prompt_template = f"""
-    You are a content writer. Write a poem of a maximum of 10 sentences on the following topic.
-    Topic: {topic}
-    Poem:
-    """
-    input_ids = tokenizer(prompt_template, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model.generate(input_ids["input_ids"], max_length=800, num_return_sequences=1)
-    blog_content = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    return blog_content
-
-# Gradio interface
-iface = gr.Interface(
-    fn=generate_blog,
+HF_TOKEN = os.getenv("HF_TOKEN")
+# Initialize the HuggingFace model
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    huggingfacehub_api_token=HF_TOKEN,
+    temperature=0.7,
+    max_new_tokens=200
+)
+# Define a prompt template for generating a blog
+TEMPLATE = """
+Write a detailed blog post on the following topic:
+Topic: {topic}
+Make sure the blog post is informative, engaging, and well-structured.
+"""
+
+# Create a prompt template instance
+blog_prompt_template = PromptTemplate(input_variables=["topic"], template=TEMPLATE)
+prompt = blog_prompt_template
+# Initialize the LLMChain
+blog_chain = llm | prompt
+
+def generate_blog_post(topic: str, author_name: str) -> str:
+    if topic:
+        # Generate the blog post
+        blog_post = blog_chain.run({"topic": topic})
+
+        # Add author name if provided
+        if author_name:
+            blog_post = f"**By {author_name}**\n\n" + blog_post
+
+        return blog_post
+    else:
+        return "Please enter a topic for the blog post."
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=generate_blog_post,
     inputs=[
-        gr.Textbox(lines=2, placeholder="Enter the poem topic", label="Blog Topic"),
+        gr.Textbox(label="Blog Topic", placeholder="Enter the topic here"),
+        gr.Textbox(label="Author Name", placeholder="Optional", optional=True)
     ],
-    outputs=gr.Textbox(label="Generated Poem"),
-    title="Poem Generator",
-    description="Generate a poem based on the providing a topic."
+    outputs="text",
+    title="AI Blog Generator",
+    description="Welcome to the AI Blog Generator. This tool allows you to generate high-quality, engaging blog posts in just a few clicks. Simply provide a topic, and the AI will create a detailed blog post for you.",
+    theme="default"
 )
 
 if __name__ == "__main__":
-    iface.launch(share=True)  # Set share=True to generate a public link
+    interface.launch()