Update app.py

app.py

@@ -1,65 +1,56 @@
 import gradio as gr
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from keybert import KeyBERT
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import requests
 
-# 🔹 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)
+# Load DeepSeek R1 model
+model_name = "deepseek-ai/deepseek-moe-8b-chat"  # DeepSeek R1 model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
 
-# 🎯 Extract interests dynamically from user input
+# 🎯 Function to extract interests 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
+    prompt = f"Extract the main interests from this request: '{text}'. Provide only 3-5 relevant words."
 
-# 🎁 Web search for gift suggestions
-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
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Run on GPU if available
+    outputs = model.generate(**inputs, max_length=100)
+    interests = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-        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 interests.split(", ")  # Return as a list of keywords
 
-        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>"
-        }
+# 🎁 Web search for gift suggestions
+def search_gifts(interests):
+    query = "+".join(interests)
+    amazon_url = f"https://www.amazon.in/s?k={query}"
+    igp_url = f"https://www.igp.com/search?q={query}"
+    indiamart_url = f"https://dir.indiamart.com/search.mp?ss={query}"
 
-    return search_links
+    return {
+        "Amazon": amazon_url,
+        "IGP": igp_url,
+        "IndiaMart": indiamart_url
+    }
 
 # 🎯 Main function for gift recommendation
 def recommend_gifts(text):
     if not text:
         return "Please enter a description."
 
-    # Extract keywords from the text
-    interests = extract_interests(text)
-
-    if not interests:
-        return {"Error": "Could not determine interests from the input."}
-
-    # Get gift suggestions
-    links = search_gifts(interests)
+    interests = extract_interests(text)  # Use DeepSeek R1
+    links = search_gifts(interests)  # Get shopping links
 
     return {
         "Predicted Interests": interests,
         "Gift Suggestions": links
     }
 
-# 🎨 Gradio UI for user interaction
+# 🎨 Gradio UI for easy interaction
 demo = gr.Interface(
     fn=recommend_gifts, 
     inputs="text", 
     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