Update app.py

app.py

@@ -1,45 +1,67 @@
 import gradio as gr
-import requests
 from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+from keybert import KeyBERT
 
-# Load NLP model (lighter model for efficiency)
+# 🔹 Load NLP models
 zero_shot = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+bert_model = SentenceTransformer("all-MiniLM-L6-v2")
+kw_model = KeyBERT(bert_model)
+
+# 🎯 Extract interests dynamically from user input
+def extract_interests(text):
+    """Extracts key interests from user input using NLP"""
+    keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 2), stop_words='english', top_n=3)
+    return [kw[0] for kw in keywords]  # Extract only the keywords
 
 # 🎁 Web search for gift suggestions
-def search_gifts(query):
-    amazon_url = f"[Amazon](https://www.amazon.in/s?k={query.replace(' ', '+')})"
-    igp_url = f"[IGP](https://www.igp.com/search?q={query.replace(' ', '+')})"
-    indiamart_url = f"[IndiaMart](https://dir.indiamart.com/search.mp?ss={query.replace(' ', '+')})"
+def search_gifts(interests):
+    """Search for gifts dynamically based on extracted keywords"""
+    search_links = {}
+    
+    for interest in interests:
+        search_query = interest.replace(" ", "+")  # Format search string
+
+        amazon_url = f"https://www.amazon.in/s?k={search_query}"
+        igp_url = f"https://www.igp.com/search?q={search_query}"
+        indiamart_url = f"https://dir.indiamart.com/search.mp?ss={search_query}"
 
-    return f"🔗 **Amazon**: {amazon_url}\n🔗 **IGP**: {igp_url}\n🔗 **IndiaMart**: {indiamart_url}"
+        search_links[interest] = {
+            "Amazon": f"<a href='{amazon_url}' target='_blank'>Amazon</a>",
+            "IGP": f"<a href='{igp_url}' target='_blank'>IGP</a>",
+            "IndiaMart": f"<a href='{indiamart_url}' target='_blank'>IndiaMart</a>"
+        }
+
+    return search_links
 
 # 🎯 Main function for gift recommendation
 def recommend_gifts(text):
     if not text:
         return "Please enter a description."
 
-    # NLP Processing
-    categories = ["art", "music", "tech", "travel", "books", "fashion", "fitness", "gaming"]
-    results = zero_shot(text, categories)
+    # Extract keywords from the text
+    interests = extract_interests(text)
 
-    # Get top interest
-    top_interest = results["labels"][0]
+    if not interests:
+        return {"Error": "Could not determine interests from the input."}
 
-    # Get gift links
-    links = search_gifts(top_interest)
+    # Get gift suggestions
+    links = search_gifts(interests)
 
-    return f"🎯 **Predicted Interest**: `{top_interest}`\n\n🛒 **Gift Suggestions:**\n{links}"
+    return {
+        "Predicted Interests": interests,
+        "Gift Suggestions": links
+    }
 
-# 🎨 Gradio UI for better display
+# 🎨 Gradio UI for user interaction
 demo = gr.Interface(
     fn=recommend_gifts, 
     inputs="text", 
-    outputs="markdown",  # 🔹 Changes output format to Markdown for better UI
+    outputs="json",
     title="🎁 AI Gift Recommender",
-    description="Enter details about the person you are buying a gift for, and get personalized suggestions with shopping links!",
+    description="Enter details about the person you are buying a gift for, and get personalized suggestions with shopping links!"
 )
 
 # 🚀 Launch Gradio App
 if __name__ == "__main__":
     demo.launch()
-