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import os, json
import gradio as gr
from huggingface_hub import InferenceClient
# Load tool prompts
with open("system_prompts.json", encoding="utf-8") as f:
SYSTEM_PROMPTS = json.load(f)
client = InferenceClient(model=os.getenv("HF_MODEL"), token=os.getenv("HF_TOKEN"))
def run_model(prompt: str, text: str) -> str:
resp = client.chat.completions.create(
messages=[{"role": "system", "content": prompt}, {"role": "user", "content": text}],
max_tokens=512,
temperature=0.3
)
return resp.choices[0].message.content
# Define one named function per tool prompt
def ecolinguistic_analysis(text: str) -> str:
"""Ecolinguistic Analysis"""
prompt = SYSTEM_PROMPTS["Ecolinguistic Analysis"]
return run_model(prompt, text)
def narrative_detection(text: str) -> str:
"""Narrative Detection"""
prompt = SYSTEM_PROMPTS["Narrative Detection"]
return run_model(prompt, text)
def critical_discourse_analysis(text: str) -> str:
"""Critical Discourse Analysis"""
prompt = SYSTEM_PROMPTS["Critical Discourse Analysis"]
return run_model(prompt, text)
def sfl_analysis(text: str) -> str:
"""SFL Analysis"""
prompt = SYSTEM_PROMPTS["SFL Analysis"]
return run_model(prompt, text)
def ecosophy_scoring(text: str) -> str:
"""Ecosophy Scoring"""
prompt = SYSTEM_PROMPTS["Ecosophy Scoring"]
return run_model(prompt, text)
# Build the Gradio interface
iface1 = gr.Interface(fn=ecolinguistic_analysis, inputs="text", outputs="text",
title="Ecolinguistic Analysis", description=ecolinguistic_analysis.__doc__)
iface2 = gr.Interface(fn=narrative_detection, inputs="text", outputs="text",
title="Narrative Detection", description=narrative_detection.__doc__)
iface3 = gr.Interface(fn=critical_discourse_analysis, inputs="text", outputs="text",
title="Critical Discourse Analysis", description=critical_discourse_analysis.__doc__)
iface4 = gr.Interface(fn=sfl_analysis, inputs="text", outputs="text",
title="SFL Analysis", description=sfl_analysis.__doc__)
iface5 = gr.Interface(fn=ecosophy_scoring, inputs="text", outputs="text",
title="Ecosophy Scoring", description=ecosophy_scoring.__doc__)
demo = gr.TabbedInterface(
interface_list=[iface1, iface2, iface3, iface4, iface5],
tab_names=list(SYSTEM_PROMPTS.keys())
)
if __name__ == "__main__":
demo.launch(mcp_server=True, share=True)
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