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README.md

@@ -1,12 +1,6 @@
 ---
-title: Keyword Paste
-emoji: 🐢
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 4.39.0
+title: keyword_paste
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.37.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import cohere
+import numpy as np
+import os
+
+# Initialize Cohere client using API key from environment variable
+co = cohere.Client(os.getenv('COHERE_API_KEY'))
+
+def calculate_relevancy(meta_title, h1_heading, first_paragraph, body, keywords):
+    # Generate embeddings for each part
+    response_meta_title = co.embed(texts=[meta_title])
+    meta_title_embedding = response_meta_title.embeddings[0]
+    
+    response_h1_heading = co.embed(texts=[h1_heading])
+    h1_heading_embedding = response_h1_heading.embeddings[0]
+    
+    response_first_paragraph = co.embed(texts=[first_paragraph])
+    first_paragraph_embedding = response_first_paragraph.embeddings[0]
+    
+    response_body = co.embed(texts=[body])
+    body_embedding = response_body.embeddings[0]
+    
+    # Generate embeddings for each keyword
+    keyword_list = [kw.strip() for kw in keywords.split(",")]
+    response_keywords = co.embed(texts=keyword_list)
+    keyword_embeddings = response_keywords.embeddings
+    
+    # Calculate relevancy score (cosine similarity) for each part
+    relevancy_scores = []
+    for kw, kw_embedding in zip(keyword_list, keyword_embeddings):
+        scores = [
+            kw,
+            np.dot(meta_title_embedding, kw_embedding) / (np.linalg.norm(meta_title_embedding) * np.linalg.norm(kw_embedding)),
+            np.dot(h1_heading_embedding, kw_embedding) / (np.linalg.norm(h1_heading_embedding) * np.linalg.norm(kw_embedding)),
+            np.dot(first_paragraph_embedding, kw_embedding) / (np.linalg.norm(first_paragraph_embedding) * np.linalg.norm(kw_embedding)),
+            np.dot(body_embedding, kw_embedding) / (np.linalg.norm(body_embedding) * np.linalg.norm(kw_embedding))
+        ]
+        relevancy_scores.append(scores)
+    return relevancy_scores
+
+# Create Gradio interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            meta_title_input = gr.Textbox(label="Meta Title", placeholder="Enter Meta Title here")
+            h1_heading_input = gr.Textbox(label="H1 Heading", placeholder="Enter H1 Heading here")
+        first_paragraph_input = gr.Textbox(label="First Paragraph", placeholder="Enter First Paragraph here")
+        body_input = gr.Textbox(label="Body", placeholder="Enter Body here", lines=5)
+        keywords_input = gr.Textbox(label="Keywords", placeholder="Enter keywords separated by commas")
+        
+        relevancy_button = gr.Button("Calculate Relevancy")
+        relevancy_output = gr.Dataframe(headers=["Keyword", "Meta Title", "H1 Heading", "First Paragraph", "Body"])
+        
+        relevancy_button.click(calculate_relevancy, 
+                               inputs=[meta_title_input, h1_heading_input, first_paragraph_input, body_input, keywords_input], 
+                               outputs=relevancy_output)
+        
+    return demo
+
+# Launch Gradio app
+demo = gradio_interface()
+demo.launch()

requirements.txt

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+gradio
+cohere
+numpy