Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import textstat
+from langchain_huggingface import HuggingFaceEndpoint
+import os
+
+# Set up Hugging Face API token and model endpoint
+HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")  # Ensure you have your token set in your environment
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    huggingfacehub_api_token=HF_TOKEN.strip(),
+    temperature=0.7,
+    max_new_tokens=200
+)
+
+def check_and_improve_seo(content):
+    # Define basic SEO criteria
+    keywords = ["SEO", "content", "optimization", "keywords", "readability"]
+    keyword_found = any(keyword.lower() in content.lower() for keyword in keywords)
+    
+    # Check readability score
+    readability_score = textstat.flesch_reading_ease(content)
+    
+    # Prepare a prompt for the LLM to improve content
+    prompt = (
+        "Optimize the following content for SEO. Ensure it includes relevant keywords, "
+        "is easy to read, and meets SEO best practices.\n\n"
+        "Content:\n" + content
+    )
+    
+    # Generate SEO-optimized content using the Hugging Face model
+    response = llm(prompt)
+    optimized_content = response['text'] if 'text' in response else response
+
+    # Define SEO checks
+    seo_checks = {
+        "Keywords Present": keyword_found,
+        "Readability Score (Flesch)": readability_score,
+        "Optimized Content": optimized_content
+    }
+    
+    return seo_checks
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=check_and_improve_seo,
+    inputs=gr.Textbox(lines=10, placeholder="Enter your content here..."),
+    outputs="json",
+    title="SEO Compatibility Checker and Optimizer",
+    description="Check if the given content is SEO compatible and get an improved version based on SEO best practices."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()