Uploading project 2 to Hugging Face Hub

README.md

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----
-title: Domain Specific Rag With Small Language Model
-emoji: 🐠
-colorFrom: green
-colorTo: pink
+
+title: Domain Specific RAG With a Small Language Model
+emoji: 🐍
+description: Search for Python tasks to retrieve verified solutions and test cases.
+colorFrom: blue
+colorTo: green
 sdk: gradio
 sdk_version: 6.5.1
 app_file: app.py
-pinned: false
----
+pinned: true
+
+🐍 Technical Code Assistant: RAG with MBPP & Phi-3
+
+🚀 Overview
+
+This project is a domain-specific Retrieval-Augmented Generation (RAG) system designed to act as a technical coding assistant. Unlike generic LLMs that may generate syntax-error-prone code, this system retrieves hand-verified solutions from Google's MBPP (Mostly Basic Python Problems) dataset and uses Microsoft Phi-3 to provide grounded, explained answers.
+
+🛠️ Technical Stack
+
+Model: Phi-3-mini-4k-instruct (3.8B parameters) — chosen for its high efficiency and reasoning capabilities in a small footprint.
+
+Vector Store: FAISS (Facebook AI Similarity Search) for high-speed local vector indexing.
+
+Embeddings: sentence-transformers/all-MiniLM-L6-v2 — optimized for asymmetric semantic search (matching natural language queries to code tasks).
+
+Data Source: google-research-datasets/mbpp (Sanitized version) — a gold-standard benchmark for Python coding tasks.
+
+🧠 The Pipeline: How it Works
+
+* The application follows a rigorous retrieval-first architecture to ensure code reliability:
+
+* Semantic Retrieval: The user enters a coding task (e.g., "Find the area of a circle"). The system encodes this query into a vector and searches the FAISS index for the most semantically similar verified task in the MBPP dataset.
+
+* Context Augmentation: The system retrieves the original human-written Python function and the associated unit tests for that task.
+
+* Grounded Generation: Phi-3 receives the retrieved code as "ground truth" and generates an explanation or adaptation based only on that verified logic.
+
+* Verification UI: The interface displays the retrieved code in a dedicated gr.Code block and lists the test cases to prove the solution is functional.
+
+🎯 Key Features for Portfolio
+
+* Small Language Model (SLM) Optimization: Demonstrates an ability to deploy high-performing systems on limited hardware (Free Tier CPU) by selecting the right model-to-task ratio.
+
+* Benchmark Grounding: Uses the MBPP dataset to eliminate hallucinations common in open-domain LLMs.
+
+* Technical UI Design: Implemented syntax highlighting and structured JSON outputs for a developer-centric user experience.
+
+👨‍💻 How to Test
+
+Enter a Python task in the text box (e.g., "Write a function to check if a number is prime").
+
+Click "Find Solution".
+
+Review the Verified Python Code and run the provided Test Cases locally to verify the output!
+
+📈 Future Enhancements
+
+[ ] Hybrid Search: Combine FAISS semantic search with BM25 keyword matching to better handle specific library names.
+
+[ ] Multi-Dataset Support: Expand the knowledge base to include LeetCode and HumanEval datasets.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+[ ] Interactive Execution: Integrate a secure sandboxed Python interpreter to run the retrieved test cases directly in the browser.

app.py

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+
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+import torch
+from datasets import load_dataset
+
+# setup model
+model_id = "microsoft/Phi-3-mini-4k-instruct"
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# load and index MBPP dataset
+print(f"[INFO] Loading MBPP Dataset...")
+dataset = load_dataset("google-research-datasets/mbpp",
+                       "sanitized",
+                       split="test")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# index the task text but store the whole dictionary as meta data
+vector_db = FAISS.from_texts(texts=[item['prompt'] for item in dataset],
+                             embedding=embeddings,
+                             metadatas=[{"code": item['code'],
+                                         "tests": item['test_list']} for item in dataset])
+
+def lookup_code(query):
+  # find the top match
+  docs = vector_db.similarity_search(query, k=1)
+  if not docs:
+    return "No match found.", "N/A", []
+
+  best_match = docs[0]
+  task_description = best_match.page_content
+  retrieved_code = best_match.metadata['code']
+  test_cases = "\n".join(best_match.metadata['tests'])
+
+  return task_description, retrieved_code, test_cases
+
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+  gr.Markdown("# 🐍 Technical Code RAG (MBPP)")
+  gr.Markdown("Search for Python tasks to retrieve verified solutions and test cases.")
+
+  with gr.Row():
+    query_input = gr.Textbox(label="Enter Task (e.g., 'find the area of circle')",
+                             placeholder="Search...")
+    search_btn = gr.Button("Find Solution",
+                           variant="primary")
+
+  with gr.Column():
+    out_desc = gr.Markdown(label="Task Description")
+
+    out_code = gr.Code(label="Verified Python Code",
+                       language="python")
+
+    out_tests = gr.Textbox(label="Validation Test Cases",
+                            lines=3)
+
+    search_btn.click(fn=lookup_code,
+                     inputs=query_input,
+                     outputs=[out_desc, out_code, out_tests])
+
+demo.launch()

requirements.txt

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+transformers
+torch
+sentence-transformers
+faiss.cpu
+langchain
+langchain_community
+gradio