Update app.py

app.py

@@ -11,68 +11,67 @@ def extract_keywords(text: str, nlp_pipeline) -> list:
 
 def generate_search_urls(keywords: list) -> dict:
     """Generate search URLs for various e-commerce platforms"""
-    # Join keywords with appropriate separators for each platform
-    amazon_query = '+'.join(keywords)
-    flipkart_query = ' '.join(keywords)
-    igp_query = '+'.join(keywords)
-    indiamart_query = ' '.join(keywords)
-    
-    # Properly encode the queries
-    amazon_query = urllib.parse.quote(amazon_query)
-    flipkart_query = urllib.parse.quote(flipkart_query)
-    igp_query = urllib.parse.quote(igp_query)
-    indiamart_query = urllib.parse.quote(indiamart_query)
-    
+    # Encode queries properly for URLs
+    query = urllib.parse.quote(" ".join(keywords))
+
     return {
-        "Amazon India": f"https://www.amazon.in/s?k={amazon_query}",
-        "Flipkart": f"https://www.flipkart.com/search?q={flipkart_query}",
-        "IGP Gifts": f"https://www.igp.com/search?q={igp_query}",
-        "IndiaMart": f"https://www.indiamart.com/find?q={indiamart_query}"
+        "Amazon India": f'<a href="https://www.amazon.in/s?k={query}" target="_blank">Amazon</a>',
+        "Flipkart": f'<a href="https://www.flipkart.com/search?q={query}" target="_blank">Flipkart</a>',
+        "IGP Gifts": f'<a href="https://www.igp.com/search?q={query}" target="_blank">IGP</a>',
+        "IndiaMart": f'<a href="https://www.indiamart.com/find?q={query}" target="_blank">IndiaMart</a>',
     }
 
 def recommend_gifts(text: str):
     """Main function to generate gift recommendations"""
     if not text:
-        return {"error": "Please provide a description."}
-    
+        return "⚠️ Please provide a description."
+
     try:
-        # Initialize the language model
+        # Load GPT-2 as a text-generation model
         nlp = pipeline(
             "text-generation",
             model="gpt2",
             device_map="auto"
         )
-        
+
         # Extract relevant keywords
         keywords = extract_keywords(text, nlp)
-        
+
         # Generate search URLs
         search_links = generate_search_urls(keywords)
-        
-        return {
-            "keywords": keywords,
-            "search_links": search_links
-        }
-        
+
+        # Format the output as clickable links
+        formatted_output = f"""
+        <h3>🔍 Predicted Interests: {", ".join(keywords)}</h3>
+        <h3>🛒 Gift Suggestions:</h3>
+        <ul>
+        <li>{search_links["Amazon India"]}</li>
+        <li>{search_links["Flipkart"]}</li>
+        <li>{search_links["IGP Gifts"]}</li>
+        <li>{search_links["IndiaMart"]}</li>
+        </ul>
+        """
+        return formatted_output
+
     except Exception as e:
-        return {"error": f"An error occurred: {str(e)}"}
+        return f"❌ Error: {str(e)}"
 
-# Create Gradio interface
+# Create Gradio interface with HTML output
 demo = gr.Interface(
     fn=recommend_gifts,
     inputs=gr.Textbox(
         lines=3,
         placeholder="Describe who you're buying a gift for (age, interests, occasion, etc.)"
     ),
-    outputs=gr.JSON(),
+    outputs=gr.HTML(),  # Change output type to HTML
     title="🎁 Smart Gift Recommender",
     description="Get personalized gift suggestions with direct shopping links!",
     examples=[
-        ["a small kid of age 3 want him to have something like toy that teaches him alphabets"],
+        ["a small kid of age 3 want him to have something like a toy that teaches alphabets"],
         ["age is 25 and he loves puzzle and online FPS games"],
         ["Looking for a gift for my mom who enjoys gardening and cooking"]
     ]
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()