Adding a simple monkey search for Leetcode - Darn LeetMonkey

app.py

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+import gradio as gr
+from pinecone import Pinecone, ServerlessSpec
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from pinecone_text.sparse import SpladeEncoder
+from sentence_transformers import SentenceTransformer
+import transformers
+transformers.logging.set_verbosity_error()
+
+# Initialize Pinecone
+PINECONE_API_KEY = "your_pinecone_api_key"
+pc = Pinecone(api_key=PINECONE_API_KEY)
+
+index_name = "leetmonkey-sparse-dense"
+index = pc.Index(index_name)
+
+# Initialize models
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+splade = SpladeEncoder(device=device)
+dense_model = SentenceTransformer('sentence-transformers/all-Mpnet-base-v2', device=device)
+
+# Load the quantized Llama 2 model and tokenizer
+model_name = "TheBloke/Llama-2-7B-Chat-GPTQ"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+def search_problems(query, top_k=5):
+    dense_query = dense_model.encode([query])[0].tolist()
+    sparse_query = splade.encode_documents([query])[0]
+
+    results = index.query(
+        vector=dense_query,
+        sparse_vector={
+            'indices': sparse_query['indices'],
+            'values': sparse_query['values']
+        },
+        top_k=top_k,
+        include_metadata=True,
+        namespace='leetcode-problems'
+    )
+
+    return results['matches']
+
+def generate_few_shot_prompt(search_results):
+    prompt = "Here are some example LeetCode problems:\n\n"
+    for result in search_results:
+        metadata = result['metadata']
+        prompt += f"Title: {metadata['title']}\n"
+        prompt += f"Topics: {', '.join(metadata['topicTags'])}\n"
+        prompt += f"Difficulty: {metadata['difficulty']}\n\n"
+    return prompt
+
+def generate_response(user_query, top_k=5):
+    search_results = search_problems(user_query, top_k)
+    few_shot_prompt = generate_few_shot_prompt(search_results)
+
+    system_prompt = """You are an AI assistant specialized in providing information about LeetCode problems. 
+    Your task is to recommend relevant problems based on the user's query and the provided examples. 
+    Focus on problem titles, difficulty levels, topic tags, and companies that have asked these problems. 
+    Do not provide specific problem solutions or content."""
+
+    user_prompt = f"Based on the following query, recommend relevant LeetCode problems:\n{user_query}"
+    full_prompt = f"{system_prompt}\n\n{few_shot_prompt}\n{user_prompt}\n\nRecommendations:"
+
+    input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to(model.device)
+    attention_mask = torch.ones_like(input_ids)
+
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=250,
+            do_sample=True,
+            top_p=0.9,
+            temperature=0.7,
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    recommendations = response.split("Recommendations:")[1].strip()
+    return recommendations
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your query about LeetCode problems..."),
+    outputs="text",
+    title="LeetCode Problem Assistant",
+    description="Ask about LeetCode problems and get structured responses based on titles, topics, and difficulty levels."
+)
+
+# Launch the app
+iface.launch()

requirements.txt

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+gradio==3.50.2
+pinecone-client==2.2.4
+transformers==4.36.2
+torch==2.1.2
+sentence-transformers==2.2.2
+pinecone-text==0.7.0
+numpy==1.26.2
+pandas==2.1.4
+networkx==3.2.1
+matplotlib==3.8.2
+torch-geometric==2.4.0