Create app.py

app.py

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+import gradio as gr
+import requests
+import json
+import os
+
+# Load system prompts
+with open("system_prompts.json", "r", encoding="utf-8") as f:
+    SYSTEM_PROMPTS = json.load(f)
+
+TOOLS = list(SYSTEM_PROMPTS.keys())
+
+# Hugging Face Endpoint details (Set these as secrets/environment variables on HF Spaces)
+HF_API_URL = os.environ.get("HF_API_URL", "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2")
+HF_API_TOKEN = os.environ.get("HF_API_TOKEN", "hf_xxx")  # Replace with your token or set as secret
+
+def query_hf_mistral(system_prompt, user_input):
+    headers = {
+        "Authorization": f"Bearer {HF_API_TOKEN}",
+        "Content-Type": "application/json"
+    }
+    # For conversational models, use the 'inputs' key with a conversation
+    payload = {
+        "inputs": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_input}
+        ],
+        "parameters": {
+            "max_new_tokens": 1024,
+            "temperature": 0.3,
+            "do_sample": False
+        }
+    }
+    response = requests.post(HF_API_URL, headers=headers, json=payload, timeout=90)
+    if response.status_code == 200:
+        result = response.json()
+        # Output parsing can differ by model, this is standard for chat endpoints
+        if isinstance(result, list) and len(result) > 0 and "generated_text" in result[0]:
+            return result[0]["generated_text"]
+        # Newer endpoints may return {'generated_text': '...'}
+        if "generated_text" in result:
+            return result["generated_text"]
+        # If it's the new chat template, output is a list of chat turns
+        if "conversation" in result and "generated_responses" in result["conversation"]:
+            return result["conversation"]["generated_responses"][-1]
+        # Fallback to string conversion
+        return str(result)
+    else:
+        return f"Error: {response.status_code}\n{response.text}"
+
+def ecoling_tool(tool, user_input):
+    system_prompt = SYSTEM_PROMPTS[tool]
+    return query_hf_mistral(system_prompt, user_input)
+
+with gr.Blocks(title="Ecolinguistic MCP Server") as demo:
+    gr.Markdown(
+        "# 🌱 Ecolinguistic MCP Server\nSelect a tool and enter your text below. Powered by Hugging Face Mistral endpoint."
+    )
+
+    with gr.Row():
+        tool = gr.Dropdown(label="Choose Ecolinguistic Tool", choices=TOOLS, value=TOOLS[0])
+        user_input = gr.Textbox(lines=7, label="Input Text", placeholder="Paste your text here...")
+
+    output = gr.Textbox(label="AI Response", lines=12)
+
+    btn = gr.Button("Analyse")
+
+    btn.click(fn=ecoling_tool, inputs=[tool, user_input], outputs=output)
+
+demo.launch()