app.py

import os
import json
import requests
import gradio as gr

# Load tool prompts
with open("system_prompts.json", "r", encoding="utf-8") as f:
    SYSTEM_PROMPTS = json.load(f)
TOOLS = list(SYSTEM_PROMPTS.keys())

# These are your HF Inference Endpoint settings
HF_API_URL = os.environ["HF_API_URL"]
HF_API_TOKEN = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"

def run_mistral(prompt: str, text: str) -> str:
    """
    Calls the Hugging Face Inference Endpoint for Mistral using REST.
    Sends 'prompt' as system message and 'text' as user message.
    For reference, HF Inference endpoints accept JSON payloads like this :contentReference[oaicite:1]{index=1}.
    """
    headers = {
        "Authorization": f"Bearer {HF_API_TOKEN}",
        "Content-Type": "application/json"
    }
    payload = {
        "inputs": [
            {"role": "system", "content": prompt},
            {"role": "user", "content": text}
        ],
        "parameters": {
            "max_new_tokens": 512,
            "temperature": 0.3,
            "return_full_text": False
        }
    }
    resp = requests.post(HF_API_URL, headers=headers, json=payload, timeout=60)
    resp.raise_for_status()
    out = resp.json()
    # HF returns a list for chat models
    if isinstance(out, list) and "generated_text" in out[0]:
        return out[0]["generated_text"]
    # else a dict
    return out.get("generated_text", json.dumps(out))

def ecoling_tool(tool: str, text: str) -> str:
    # Looks up the prompt and calls the endpoint
    return run_mistral(SYSTEM_PROMPTS[tool], text)

demo = gr.Interface(
    fn=ecoling_tool,
    inputs=[gr.Dropdown(TOOLS, label="Tool"), gr.Textbox(lines=8, label="Input Text")],
    outputs=[gr.Textbox(label="Response")],
    title="🌱 Ecolinguistic MCP Server",
    description="Accessible via UI and standard MCP; powered by a HF Mistral endpoint."
)

if __name__ == "__main__":
    demo.launch(mcp_server=True)