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title: SolarAI
emoji: 🌖
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
short_description: It is an AI-powered chatbot.
---
# 🌞 SolarAI Chatbot
This project is an **AI-powered chatbot** that provides accurate and insightful information about the **solar industry**, including **solar panel technology, installation processes, maintenance, costs, ROI analysis, and market trends**. The chatbot integrates **LLM (ChatGroq - Mixtral-8x7B)** with **vector search (FAISS)** for better context-aware responses.
## 📌 Features
- Extracts solar panels knowledge from a **DOCX file** (manually created from data available on internet)
- Converts text into **embeddings** using `SentenceTransformer`
- Stores embeddings in a **FAISS vector database** for efficient retrieval
- Queries relevant information before sending it to **ChatGroq (Mixtral-8x7B)**
- Provides an **interactive chatbot UI using Gradio**
---
## 🛠 Installation & Setup
### **Step 1: Install Dependencies**
```bash
pip install -r requirements.txt
```
### **Step 2: Run the Chatbot**
```bash
python app.py
```
---
## 📂 Code Breakdown (Function-by-Function)
### **1️⃣ Extracting Text from DOCX**
```python
def extract_text_from_docx(file_path):
doc = Document(file_path)
text = "\n".join([para.text for para in doc.paragraphs if para.text.strip()])
return text
```
🔹 **Purpose:** Reads a `.docx` file and extracts useful solar-related information.
---
### **2️⃣ Splitting Text into Chunks**
```python
def split_text(text, chunk_size=300):
sentences = text.split(". ")
chunks, current_chunk = [], ""
for sentence in sentences:
if len(current_chunk) + len(sentence) < chunk_size:
current_chunk += sentence + ". "
else:
chunks.append(current_chunk.strip())
current_chunk = sentence + ". "
if current_chunk:
chunks.append(current_chunk.strip())
return chunks
```
🔹 **Purpose:** Splits large text data into smaller, meaningful **chunks** for better vector search performance.
---
### **3️⃣ Generating Embeddings**
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2") # Embedding model
embeddings = model.encode(chunks)
```
🔹 **Purpose:** Converts text **chunks** into numerical representations (vectors) for similarity search.
---
### **4️⃣ Storing Embeddings in FAISS Vector Database**
```python
import faiss
import numpy as np
vector_dim = embeddings.shape[1]
index = faiss.IndexFlatL2(vector_dim)
index.add(np.array(embeddings))
faiss.write_index(index, "solar_vectors.index")
```
🔹 **Purpose:** Uses **FAISS** to efficiently store and retrieve relevant text when a user asks a question.
---
### **5️⃣ Retrieving Relevant Information**
```python
def retrieve_relevant_text(query, top_k=2):
query_embedding = model.encode([query])
distances, indices = index.search(np.array(query_embedding), top_k)
return " ".join([chunks[i] for i in indices[0]])
```
🔹 **Purpose:** Finds the **most relevant** pieces of information to **pass to the chatbot** before generating a response.
---
### **6️⃣ Chatbot Integration with ChatGroq (Mixtral-8x7B)**
```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
llm = ChatGroq(model="mixtral-8x7b-32768", temperature=0.2)
def chat_with_groq(user_query):
retrieved_text = retrieve_relevant_text(user_query)
system_message = "You are an AI assistant that provides accurate solar energy information."
prompt_template = ChatPromptTemplate.from_messages([
("system", system_message),
("human", f"Use the following information to answer: {retrieved_text} \n\nUser Query: {user_query}")
])
chain = prompt_template | llm
response = chain.invoke({"text": user_query})
return response.content
```
🔹 **Purpose:** Uses **retrieved data + user query** to generate an **LLM-based response**.
---
### **7️⃣ Gradio Chatbot UI**
```python
import gradio as gr
def gradio_chatbot(user_input):
response = chat_with_groq(user_input)
return response
with gr.Blocks() as demo:
gr.Markdown("# 🌞 SolarAI 🌞")
with gr.Row():
user_input = gr.Textbox(placeholder="Ask me anything about solar energy...", lines=2, interactive=True)
with gr.Row():
output_box = gr.Textbox(lines=6, interactive=True, label="Chatbot Response")
submit_btn = gr.Button("Ask")
submit_btn.click(fn=gradio_chatbot, inputs=user_input, outputs=output_box)
demo.launch()
```
🔹 **Purpose:** Creates a **Gradio-powered UI** for user interaction with the chatbot.
---
## 🚀 Deployment Guide
### **Option 1: Run Locally**
```bash
python app.py
```
### **Option 2: Deploy on Hugging Face Spaces**
1. Create `requirements.txt`.
2. Push to Hugging Face:
```bash
git init
git add .
git commit -m "Deploy Solar Chatbot"
git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/solar-chatbot
git push origin main
```
✅ Your chatbot is now **LIVE** on Hugging Face Spaces!
---
## 🎯 Future Improvements
✅ Add **voice-based interaction** 🎙️ ✅ Improve **multi-turn conversation memory** ✅ Enable **real-time solar industry data fetching** ✅ Integrate **WhatsApp/Telegram bot support** 📲
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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