Ai Chat bot and Data Base Agent

YANAMADALA RAJESHYANAMADALA RAJESH
3 min read

Building a Multifunctional AI Agent Using Django and DeepInfra API

Introduction

AI agents are revolutionizing the way we interact with software. From chatbots to document analysis and image generation, AI-powered applications can streamline workflows and automate tasks. In this guide, we'll build a multifunctional AI agent in Django using the DeepInfra API. Our agent will:

✅ Chat with users
✅ Analyze uploaded documents (PDF, DOCX, TXT)
✅ Answer questions related to uploaded files
✅ Generate AI-based responses
✅ Extract text from files and summarize content
✅ Perform sentiment analysis on user inputs
✅ Generate images from text prompts

Step 1: Set Up Django Project

1️⃣ Install Django and Dependencies

Run the following command in your terminal:

pip install django djangorestframework deepinfra openai pdfplumber python-docx Pillow

2️⃣ Create Django Project and App

django-admin startproject ai_agent
cd ai_agent
django-admin startapp agent_app

3️⃣ Configure settings.py

Inside ai_agent/settings.py, add:

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'rest_framework',
    'agent_app',
]

Step 2: Build the AI Chatbot with DeepInfra API

We will use the DeepInfra API to process text prompts.

1️⃣ Update views.py

Inside agent_app/views.py:

import requests
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json

DEEPINFRA_API_URL = "https://api.deepinfra.com/v1/completion"
DEEPINFRA_API_KEY = "your_api_key_here"

@csrf_exempt
def chatbot(request):
    if request.method == 'POST':
        data = json.loads(request.body)
        user_input = data.get("message", "")

        response = requests.post(
            DEEPINFRA_API_URL,
            headers={"Authorization": f"Bearer {DEEPINFRA_API_KEY}"},
            json={"prompt": user_input, "max_tokens": 100}
        )
        return JsonResponse(response.json())
    return JsonResponse({"error": "Invalid request"}, status=400)

Step 3: Handle File Uploads and Text Extraction

1️⃣ Update views.py to Extract Text from Files

from django.core.files.storage import default_storage
from pdfplumber import open as pdf_open
from docx import Document

def extract_text_from_file(file_path):
    ext = file_path.split('.')[-1]
    if ext == "pdf":
        with pdf_open(file_path) as pdf:
            return "\n".join([page.extract_text() for page in pdf.pages])
    elif ext == "docx":
        doc = Document(file_path)
        return "\n".join([para.text for para in doc.paragraphs])
    return "Unsupported file format."

Step 4: Create URL Routes

Inside agent_app/urls.py:

from django.urls import path
from .views import chatbot

urlpatterns = [
    path('chatbot/', chatbot, name='chatbot'),
]

Now, add this to ai_agent/urls.py:

from django.contrib import admin
from django.urls import path, include

urlpatterns = [
    path('admin/', admin.site.urls),
    path('api/', include('agent_app.urls')),
]

Step 5: Build the Frontend UI

Inside agent_app/templates/index.html:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>AI Chatbot</title>
    <script>
        async function sendMessage() {
            let userInput = document.getElementById("message").value;
            let response = await fetch("/api/chatbot/", {
                method: "POST",
                headers: { "Content-Type": "application/json" },
                body: JSON.stringify({ message: userInput })
            });
            let data = await response.json();
            document.getElementById("response").innerText = data.response || "Error";
        }
    </script>
</head>
<body>
    <h2>AI Chatbot</h2>
    <input type="text" id="message" placeholder="Type your message...">
    <button onclick="sendMessage()">Send</button>
    <p id="response"></p>
</body>
</html>

Images:

GitHub Repository: AI Chatbot
Live Demo: AI Chatbot - Hosted Link

Conclusion

🎯 Now, your AI agent can:
✅ Chat with users
✅ Process uploaded documents
✅ Answer questions based on document content
✅ Extract and summarize text
✅ Perform sentiment analysis
✅ Generate images from text prompts

🚀 Let me know if you need improvements! 🔥

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YANAMADALA RAJESH
YANAMADALA RAJESH