Update app.py

app.py

@@ -3,51 +3,50 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 import requests
 
-# Load DeepSeek R1 model with Accelerate enabled
-model_name = "deepseek-ai/deepseek-moe-8b-chat"
+# Load DeepSeek R1 model
+model_name = "deepseek-ai/DeepSeek-R1"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
-    model_name, 
-    torch_dtype=torch.float16, 
-    device_map="auto", 
-    trust_remote_code=True, 
-    low_cpu_mem_usage=True  # Helps reduce memory consumption
+    model_name, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
 )
 
-# 🎯 Extract interests using DeepSeek R1
+# 🎯 Function to extract interests from user input
 def extract_interests(text):
-    prompt = f"Extract key interests from this request: '{text}'. Give 3-5 relevant words only."
+    prompt = f"Extract 3-5 relevant gift-related interests from: '{text}'"
 
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Runs on GPU if available
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Use GPU if available
     outputs = model.generate(**inputs, max_length=100)
     interests = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     return interests.split(", ")  # Return as a list of keywords
 
-# 🎁 Search for gift suggestions from various e-commerce sites
+# 🎁 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 {
-        "Amazon": f"https://www.amazon.in/s?k={query}",
-        "Flipkart": f"https://www.flipkart.com/search?q={query}",
-        "IGP": f"https://www.igp.com/search?q={query}",
-        "IndiaMart": f"https://dir.indiamart.com/search.mp?ss={query}"
+        "Amazon": f"[Amazon]({amazon_url})",
+        "IGP": f"[IGP]({igp_url})",
+        "IndiaMart": f"[IndiaMart]({indiamart_url})"
     }
 
-# 🎯 Main function for generating gift recommendations
+# 🎯 Main function for gift recommendation
 def recommend_gifts(text):
     if not text:
         return "Please enter a description."
 
-    interests = extract_interests(text)  # Extract relevant interests
-    links = search_gifts(interests)  # Generate shopping links
+    interests = extract_interests(text)  # Use DeepSeek R1
+    links = search_gifts(interests)  # Get shopping links
 
     return {
         "Predicted Interests": interests,
         "Gift Suggestions": links
     }
 
-# 🎨 Gradio Interface
+# 🎨 Gradio UI for easy interaction
 demo = gr.Interface(
     fn=recommend_gifts, 
     inputs="text",