Update app.py

app.py

@@ -1,58 +1,35 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-import requests
 
-# Load DeepSeek R1 model
-model_name = "deepseek-ai/deepseek-moe-8b-chat"  # DeepSeek R1 model
+# Load DeepSeek-R1 model and tokenizer
+model_name = "deepseek-ai/DeepSeek-R1"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
 
-# 🎯 Function to extract interests from user input
-def extract_interests(text):
-    prompt = f"Extract the main interests from this request: '{text}'. Provide only 3-5 relevant words."
-
-    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)
-
-    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}"
-    igp_url = f"https://www.igp.com/search?q={query}"
-    indiamart_url = f"https://dir.indiamart.com/search.mp?ss={query}"
-
-    return {
-        "Amazon": amazon_url,
-        "IGP": igp_url,
-        "IndiaMart": indiamart_url
-    }
-
-# 🎯 Main function for gift recommendation
+# Function to generate gift recommendations
 def recommend_gifts(text):
     if not text:
         return "Please enter a description."
 
-    interests = extract_interests(text)  # Use DeepSeek R1
-    links = search_gifts(interests)  # Get shopping links
+    # Prepare input prompt for the model
+    prompt = f"Based on the following description, suggest suitable gifts: '{text}'"
+
+    # Tokenize input and generate response
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(inputs.input_ids, max_length=200, do_sample=True)
+    recommendation = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    return {
-        "Predicted Interests": interests,
-        "Gift Suggestions": links
-    }
+    return {"Recommendation": recommendation}
 
-# 🎨 Gradio UI for easy interaction
+# Gradio interface
 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!",
+    title="AI Gift Recommender",
+    description="Enter details about the person you are buying a gift for, and get personalized suggestions!",
 )
 
-# 🚀 Launch Gradio App
 if __name__ == "__main__":
     demo.launch()