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app.py

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+
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+import uvicorn
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+import json
+import google.generativeai as genai
+from tools import calculator, email_drafter, pdf_qa, code_inspector
+
+# Configure Gemini API
+try:
+    genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+    model = genai.GenerativeModel('gemini-2.5-flash')
+except Exception as e:
+    print(f"Error configuring Gemini or loading model: {e}")
+    model = None # Set model to None if configuration fails
+
+app = FastAPI()
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root():
+    with open("templates/index.html") as f:
+        return HTMLResponse(content=f.read(), status_code=200)
+
+@app.post("/mcp")
+async def mcp_endpoint(prompt: str = Form(...), file: UploadFile = File(None)):
+    if file:
+        file_path = f"temp_{file.filename}"
+        with open(file_path, "wb") as buffer:
+            buffer.write(await file.read())
+        
+        if "pdf" in file.content_type:
+            response = pdf_qa.answer_pdf_question(file_path, prompt, model)
+        else:
+            response = "Unsupported file type."
+        
+        os.remove(file_path)
+        return JSONResponse(content={"response": response})
+
+    tool_prompts = json.load(open("prompts/tool_prompts.json"))
+
+    if "sum" in prompt.lower():
+        response = calculator.calculate(prompt, "sum")
+    elif "multiply" in prompt.lower():
+        response = calculator.calculate(prompt, "multiply")
+    elif "subtract" in prompt.lower():
+        response = calculator.calculate(prompt, "subtract")
+    elif "divide" in prompt.lower():
+        response = calculator.calculate(prompt, "divide")
+    elif "draft an email" in prompt.lower():
+        response = email_drafter.draft_email(prompt, model)
+    elif "check code" in prompt.lower():
+        response = code_inspector.check_code(prompt, model)
+    else:
+        response = "I'm sorry, I don't understand that request."
+
+    return JSONResponse(content={"response": response})
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=7860)

prompts/tool_prompts.json

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+{
+    "calculator": {
+        "sum": "To calculate the sum of two numbers, please say something like 'sum 5 and 3'.",
+        "multiply": "To multiply two numbers, please say something like 'multiply 5 by 3'.",
+        "subtract": "To subtract two numbers, please say something like 'subtract 5 from 3'.",
+        "divide": "To divide two numbers, please say something like 'divide 5 by 3'."
+    },
+    "email_drafter": {
+        "draft": "To draft an email, please say something like 'draft an email to [recipient] about [subject and body]'."
+    },
+    "pdf_qa": {
+        "answer": "To answer questions about a PDF, please upload a PDF file and ask your question."
+    },
+    "code_inspector": {
+        "check": "To check Python code for syntax and indentation errors, please say 'check code: [your python code]'."
+    }
+}

requirements.txt

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+fastapi
+uvicorn
+python-multipart
+Jinja2
+PyPDF2
+google-generativeai
+qdrant-client
+sentence-transformers
+python-dotenv

static/script.js

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+document.getElementById('mcp-form').addEventListener('submit', async (e) => {
+    e.preventDefault();
+
+    const promptInput = document.getElementById('prompt-input');
+    const fileInput = document.getElementById('file-input');
+    const chatBox = document.getElementById('chat-box');
+
+    const prompt = promptInput.value;
+    const file = fileInput.files[0];
+
+    const formData = new FormData();
+    formData.append('prompt', prompt);
+    if (file) {
+        formData.append('file', file);
+    }
+
+    // Display user message
+    const userMessageDiv = document.createElement('div');
+    userMessageDiv.classList.add('message', 'user-message');
+    userMessageDiv.textContent = prompt;
+    chatBox.appendChild(userMessageDiv);
+
+    promptInput.value = '';
+    fileInput.value = '';
+
+    try {
+        const response = await fetch('/mcp', {
+            method: 'POST',
+            body: formData,
+        });
+        const data = await response.json();
+
+        // Display MCP response
+        const mcpMessageDiv = document.createElement('div');
+        mcpMessageDiv.classList.add('message', 'mcp-message');
+        mcpMessageDiv.textContent = data.response;
+        chatBox.appendChild(mcpMessageDiv);
+
+        chatBox.scrollTop = chatBox.scrollHeight; // Scroll to bottom
+
+    } catch (error) {
+        console.error('Error:', error);
+        const errorMessageDiv = document.createElement('div');
+        errorMessageDiv.classList.add('message', 'mcp-message');
+        errorMessageDiv.textContent = 'Error: Could not connect to the server.';
+        chatBox.appendChild(errorMessageDiv);
+        chatBox.scrollTop = chatBox.scrollHeight; // Scroll to bottom
+    }
+});

static/style.css

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+body {
+    font-family: Arial, sans-serif;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    min-height: 100vh;
+    background-color: #f4f4f4;
+    margin: 0;
+}
+
+.container {
+    background-color: #fff;
+    padding: 20px;
+    border-radius: 8px;
+    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    width: 90%;
+    max-width: 600px;
+}
+
+h1 {
+    text-align: center;
+    color: #333;
+}
+
+.chat-box {
+    border: 1px solid #ddd;
+    padding: 10px;
+    height: 300px;
+    overflow-y: scroll;
+    margin-bottom: 10px;
+    border-radius: 4px;
+    background-color: #e9e9e9;
+}
+
+.message {
+    margin-bottom: 8px;
+    padding: 5px 10px;
+    border-radius: 4px;
+}
+
+.user-message {
+    background-color: #dcf8c6;
+    text-align: right;
+}
+
+.mcp-message {
+    background-color: #c6e8f8;
+    text-align: left;
+}
+
+form {
+    display: flex;
+    gap: 10px;
+}
+
+input[type="text"],
+input[type="file"] {
+    flex-grow: 1;
+    padding: 10px;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+}
+
+button {
+    padding: 10px 15px;
+    background-color: #007bff;
+    color: white;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+}
+
+button:hover {
+    background-color: #0056b3;
+}

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>MCP Server</title>
+    <link rel="stylesheet" href="/static/style.css">
+</head>
+<body>
+    <div class="container">
+        <h1>MCP Server Interaction</h1>
+        <div class="chat-box" id="chat-box"></div>
+        <form id="mcp-form">
+            <input type="text" id="prompt-input" placeholder="Enter your prompt..." required>
+            <input type="file" id="file-input">
+            <button type="submit">Send</button>
+        </form>
+    </div>
+    <script src="/static/script.js"></script>
+</body>
+</html>

tools/calculator.py

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+def calculate(prompt, operation):
+    try:
+        # Extract numbers from the prompt
+        numbers = [int(s) for s in prompt.split() if s.isdigit()]
+        if len(numbers) < 2:
+            return "Please provide at least two numbers for calculation."
+
+        num1 = numbers[0]
+        num2 = numbers[1]
+
+        if operation == "sum":
+            result = num1 + num2
+            return f"The sum of {num1} and {num2} is {result}."
+        elif operation == "multiply":
+            result = num1 * num2
+            return f"The product of {num1} and {num2} is {result}."
+        elif operation == "subtract":
+            result = num1 - num2
+            return f"The difference between {num1} and {num2} is {result}."
+        elif operation == "divide":
+            if num2 == 0:
+                return "Cannot divide by zero."
+            result = num1 / num2
+            return f"The division of {num1} by {num2} is {result}."
+        else:
+            return "Invalid operation."
+    except Exception as e:
+        return f"Error during calculation: {e}"

tools/code_inspector.py

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+import google.generativeai as genai
+
+def check_code(prompt, model):
+    code_prefix = "check code:"
+    if code_prefix in prompt.lower():
+        code = prompt.lower().split(code_prefix, 1)[1].strip()
+    else:
+        return "Please provide the Python code after 'check code:'."
+
+    try:
+        response = model.generate_content(f"Analyze the following Python code for syntax errors, logical errors, and best practice violations. Provide detailed explanations and suggest fixes:\n\n```python\n{code}\n```")
+        return response.text
+    except Exception as e:
+        return f"Error checking code with Gemini: {e}"

tools/email_drafter.py

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+import google.generativeai as genai
+
+def draft_email(prompt, model):
+    try:
+        response = model.generate_content(f"Draft an email based on the following request: {prompt}")
+        return response.text
+    except Exception as e:
+        return f"Error drafting email with Gemini: {e}"

tools/pdf_qa.py

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+import PyPDF2
+import google.generativeai as genai
+from qdrant_client import QdrantClient, models
+from sentence_transformers import SentenceTransformer
+import os
+
+# Initialize Qdrant client and Sentence Transformer model
+client = QdrantClient(":memory:")  # Use in-memory Qdrant for simplicity
+encoder = SentenceTransformer('all-MiniLM-L6-v2')
+
+COLLECTION_NAME = "pdf_documents"
+
+def create_collection_if_not_exists():
+    try:
+        client.get_collection(collection_name=COLLECTION_NAME)
+    except Exception:
+        client.create_collection(
+            collection_name=COLLECTION_NAME,
+            vectors_config=models.VectorParams(size=encoder.get_sentence_embedding_dimension(), distance=models.Distance.COSINE),
+        )
+
+create_collection_if_not_exists()
+
+def answer_pdf_question(pdf_path, question, gemini_model):
+    try:
+        # Extract text from PDF
+        text = ""
+        with open(pdf_path, 'rb') as file:
+            reader = PyPDF2.PdfReader(file)
+            for page_num in range(len(reader.pages)):
+                text += reader.pages[page_num].extract_text()
+        
+        # Chunk text and generate embeddings
+        chunks = [text[i:i+500] for i in range(0, len(text), 500)] # Simple chunking
+        points = []
+        for i, chunk in enumerate(chunks):
+            points.append(
+                models.PointStruct(
+                    id=i,
+                    vector=encoder.encode(chunk).tolist(),
+                    payload={"text": chunk}
+                )
+            )
+        
+        client.upsert(
+            collection_name=COLLECTION_NAME,
+            points=points,
+            wait=True
+        )
+
+        # Search for relevant chunks
+        query_vector = encoder.encode(question).tolist()
+        search_result = client.search(
+            collection_name=COLLECTION_NAME,
+            query_vector=query_vector,
+            limit=3  # Retrieve top 3 most relevant chunks
+        )
+
+        context = " ".join([hit.payload['text'] for hit in search_result])
+
+        # Use Gemini to answer the question based on context
+        response = gemini_model.generate_content(f"Context: {context}\n\nQuestion: {question}\n\nAnswer:")
+        return response.text
+
+    except Exception as e:
+        return f"Error processing PDF or answering question: {e}"