app.py

import gradio as gr
import requests
import os
import json

# --- Configuration ---
# API configuration but hidden from end users
API_URL = "https://router.huggingface.co/nebius/v1/chat/completions"
MODEL_ID = "google/gemma-3-27b-it-fast"  # Real model hidden from users
PUBLIC_MODEL_NAME = "AgriAssist_LLM"     # What users will see
HF_TOKEN = os.getenv("HF_TOKEN")

if not HF_TOKEN:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable or secret.")

HEADERS = {"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}

# --- Logging that doesn't expose real model name ---
print(f"Application configured to use {PUBLIC_MODEL_NAME}.")
print(f"API Endpoint configured.")

# --- Inference Function (Using Hugging Face API) ---
def generate_response(prompt, max_new_tokens=512):
    print(f"Received prompt: {prompt}")
    print(f"Preparing payload for API...")

    payload = {
        "messages": [
            {
                "role": "user",
                "content": prompt
            }
        ],
        "model": MODEL_ID,  # Real model used in API call
        "max_tokens": max_new_tokens,
    }

    print(f"Sending request to API for {PUBLIC_MODEL_NAME}...")
    try:
        response = requests.post(API_URL, headers=HEADERS, json=payload)
        response.raise_for_status()
        result = response.json()
        print(f"{PUBLIC_MODEL_NAME} Response Received Successfully.")

        if "choices" in result and len(result["choices"]) > 0 and "message" in result["choices"][0] and "content" in result["choices"][0]["message"]:
            api_response_content = result["choices"][0]["message"]["content"]
            print(f"{PUBLIC_MODEL_NAME} generated content")
            return api_response_content
        else:
            print(f"Unexpected API response structure")
            return f"Error: {PUBLIC_MODEL_NAME} encountered an issue processing your request. Please try again."

    except requests.exceptions.RequestException as e:
        error_detail = ""
        if e.response is not None:
            try:
                error_detail = e.response.json()
            except json.JSONDecodeError:
                error_detail = e.response.text
        print(f"Error calling API: {e}")
        return f"{PUBLIC_MODEL_NAME} is currently experiencing connectivity issues. Please try again later."

    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        return f"{PUBLIC_MODEL_NAME} encountered an unexpected error. Please try again later."

# --- Gradio Interface ---
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=5, label="Enter your prompt", placeholder="Type your question or instruction here..."),
    outputs=gr.Textbox(lines=8, label=f"{PUBLIC_MODEL_NAME} Response:"),
    title=f"Chat with {PUBLIC_MODEL_NAME}",
    description=(f"This demo connects you with {PUBLIC_MODEL_NAME}, a specialized agricultural assistant. "
                "Submit your farming, crop management, or agricultural questions below."),
    allow_flagging="never",
    examples=[
        ["What are sustainable practices for improving soil health in organic farming?"],
        ["Explain crop rotation benefits and scheduling for small vegetable farms."],
        ["How can I identify and treat common tomato plant diseases?"],
        ["What irrigation methods are most water-efficient for drought-prone regions?"],
    ]
)

# --- Launch the App ---
iface.launch(server_name="0.0.0.0", server_port=7860)