README.md

metadata

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

pip install -r requirements.txt

Step 2: Run the Chatbot

python app.py

---

📂 Code Breakdown (Function-by-Function)

1️⃣ Extracting Text from DOCX

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

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

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

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

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)

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

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

python app.py

Option 2: Deploy on Hugging Face Spaces

1. Create requirements.txt.

2. Push to Hugging Face:

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