Update app.py

app.py

@@ -1,44 +1,57 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import torch
 import requests
 
-# Load DeepSeek R1 model
+# Load DeepSeek-R1 model
 model_name = "deepseek-ai/DeepSeek-R1"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# Ensure the model uses float16 instead of fp8
 model = AutoModelForCausalLM.from_pretrained(
-    model_name, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
+    model_name,
+    torch_dtype=torch.float16,  # Forces float16 to prevent fp8 issue
+    device_map="auto",
+    trust_remote_code=True
 )
 
+# Use a text-generation pipeline for better inference
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+
+
 # 🎯 Function to extract interests from user input
 def extract_interests(text):
-    prompt = f"Extract 3-5 relevant gift-related interests from: '{text}'"
+    prompt = f"Extract 3-5 relevant interests from this request: '{text}'. Focus on hobbies and product preferences."
 
-    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)
+    # Generate model output
+    response = generator(prompt, max_length=50, num_return_sequences=1)
+    interests = response[0]["generated_text"].replace(prompt, "").strip()
+
+    return interests.split(", ")  # Convert to a list of keywords
 
-    return interests.split(", ")  # Return as a list of keywords
 
 # 🎁 Web search for gift suggestions
 def search_gifts(interests):
     query = "+".join(interests)
     amazon_url = f"https://www.amazon.in/s?k={query}"
+    flipkart_url = f"https://www.flipkart.com/search?q={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"[Amazon]({amazon_url})",
-        "IGP": f"[IGP]({igp_url})",
-        "IndiaMart": f"[IndiaMart]({indiamart_url})"
+        "Amazon": amazon_url,
+        "Flipkart": flipkart_url,
+        "IGP": igp_url,
+        "IndiaMart": indiamart_url
     }
 
+
 # 🎯 Main function for gift recommendation
 def recommend_gifts(text):
     if not text:
         return "Please enter a description."
 
-    interests = extract_interests(text)  # Use DeepSeek R1
+    interests = extract_interests(text)  # Extract interests using DeepSeek R1
     links = search_gifts(interests)  # Get shopping links
 
     return {
@@ -46,10 +59,11 @@ def recommend_gifts(text):
         "Gift Suggestions": links
     }
 
+
 # 🎨 Gradio UI for easy interaction
 demo = gr.Interface(
-    fn=recommend_gifts, 
-    inputs="text", 
+    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!",