Upload 3 files

README.md

@@ -1,12 +1,29 @@
----
-title: amazoncomparisontool
-emoji: 🐳
-colorFrom: gray
-colorTo: blue
-sdk: static
-pinned: false
-tags:
-  - deepsite
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Product Search Agent
+
+This is a fully free, deployable AI tool built with Streamlit and LangChain that searches Amazon cellphone accessories using natural language.
+
+## Features
+
+- Natural language understanding
+- Vector search (FAISS + sentence-transformers)
+- Price and keyword filtering
+- Conversational agent interface
+- Deployed on Hugging Face Spaces
+
+## How to Run
+
+### Locally
+
+```bash
+pip install -r requirements.txt
+streamlit run app.py
+```
+
+### Hugging Face Spaces
+
+1. Create a new Space (Streamlit)
+2. Upload all files
+3. Add your `secrets.toml` with an OpenRouter or OpenAI API key
+4. Deploy!
+
+Enjoy your free AI agent!

app.py

@@ -0,0 +1,139 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import faiss
+import re
+import ast
+import os
+import urllib.request
+
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+from langchain.chat_models import ChatOpenAI
+from langchain.agents import initialize_agent, AgentType, tool
+from streamlit_chat import message
+
+# ---------------------------
+# Configuration
+# ---------------------------
+st.set_page_config(page_title="📱 AI Product Search Agent", layout="wide")
+
+# ---------------------------
+# Load model
+# ---------------------------
+@st.cache_resource
+def load_model():
+    return SentenceTransformer("all-MiniLM-L6-v2")
+
+# ---------------------------
+# Load dataset and FAISS index
+# ---------------------------
+@st.cache_data
+def load_data():
+    parquet_url = "https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023/resolve/main/raw_meta_Cell_Phones_and_Accessories/full-00000-of-00007.parquet"
+    df = pd.read_parquet(parquet_url)
+
+    index_url = "https://huggingface.co/GovinKin/MGTA415database/resolve/main/cellphones_index.faiss"
+    local_index_path = "cellphones_index.faiss"
+    if not os.path.exists(local_index_path):
+        urllib.request.urlretrieve(index_url, local_index_path)
+
+    index = faiss.read_index(local_index_path)
+    return df, index
+
+# ---------------------------
+# Search functions
+# ---------------------------
+def search(query, model, df, index, top_k=10):
+    query_vector = model.encode([query]).astype("float32")
+    distances, indices = index.search(query_vector, k=top_k)
+    results = df.iloc[indices[0]].copy()
+    results["distance"] = distances[0]
+    return results
+
+def search_plus(query, model, df, index, top_k=20):
+    results = search(query, model, df, index, top_k=top_k)
+
+    price_match = re.search(r"(under|below)\s*\$?(\d+)", query.lower())
+    price_under = float(price_match.group(2)) if price_match else None
+
+    if price_under:
+        try:
+            results["price"] = results["price"].astype(float)
+            results = results[results["price"] < price_under]
+        except:
+            pass
+
+    stop_words = {"i", "want", "need", "the", "a", "for", "with", "to", "is", "it", "on", "of", "buy", "and", "in"}
+    keywords = [kw for kw in query.lower().split() if kw not in stop_words and len(kw) > 2]
+
+    if not results.empty and keywords:
+        pattern = '|'.join(map(re.escape, keywords))
+        results = results[results["title"].str.lower().str.contains(pattern, na=False)]
+
+    return results
+
+def rerank_by_similarity(query, results, model, top_n=5):
+    if results.empty:
+        return results
+    query_vec = model.encode([query], convert_to_tensor=True)
+    titles = results["title"].astype(str).tolist()
+    title_vecs = model.encode(titles, convert_to_tensor=True)
+    scores = cos_sim(query_vec, title_vecs)[0].cpu().numpy()
+    results["similarity"] = scores
+    return results.sort_values("similarity", ascending=False).head(top_n)
+
+# ---------------------------
+# Agent Tool: wraps search_plus
+# ---------------------------
+@tool
+def product_search_tool(query: str) -> str:
+    """Search for cellphone accessories using a natural query."""
+    results = search_plus(query, model, df_all, index, top_k=10)
+    if results.empty:
+        return "No results found."
+    return "\n".join(results["title"].head(5).tolist())
+
+# ---------------------------
+# Load all resources
+# ---------------------------
+model = load_model()
+df_all, index = load_data()
+
+# ---------------------------
+# Agent setup
+# ---------------------------
+import os
+os.environ["OPENAI_API_KEY"] = st.secrets["openai"]["api_key"]
+os.environ["OPENAI_API_BASE"] = st.secrets["openai"].get("base_url", "https://api.openai.com/v1")
+
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.3)
+agent = initialize_agent(
+    tools=[product_search_tool],
+    llm=llm,
+    agent=AgentType.OPENAI_FUNCTIONS,
+    verbose=True
+)
+
+# ---------------------------
+# Streamlit Chat Interface
+# ---------------------------
+st.title("🤖 AI Product Search Agent")
+st.markdown("Ask natural questions like 'cheap rugged iPhone case under $30'")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+user_input = st.chat_input("Ask about cellphone accessories...")
+
+if user_input:
+    st.session_state.chat_history.append(("user", user_input))
+    with st.spinner("Agent is thinking..."):
+        try:
+            reply = agent.run(user_input)
+        except Exception as e:
+            reply = f"⚠️ Agent error: {e}"
+    st.session_state.chat_history.append(("agent", reply))
+
+for role, msg in st.session_state.chat_history:
+    message(msg, is_user=(role == "user"))

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+streamlit-chat
+langchain
+openai
+sentence-transformers
+faiss-cpu
+pandas
+numpy