Update README.md

README.md

@@ -9,5 +9,195 @@ app_file: app.py
 pinned: false
 short_description: It is an AI-powered chatbot.
 ---
+# 🌞 Solar Industry 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 energy knowledge from a **DOCX file**
+- 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** 📲
+
+🚀 **Enjoy your Solar Industry AI Assistant!** ☀️
+
+
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference