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| import gradio as gr | |
| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from huggingface_hub import hf_hub_download, HfApi | |
| import joblib | |
| import os | |
| from datetime import datetime, timedelta | |
| import pandas as pd | |
| REPO_ID = "GodfreyOwino/NPK_needs_mode2" | |
| FILENAME = "npk_needs_model.joblib" | |
| UPDATE_FREQUENCY = timedelta(days=1) | |
| class InputData(BaseModel): | |
| crop_name: str | |
| target_yield: float | |
| field_size: float | |
| ph: float | |
| organic_carbon: float | |
| nitrogen: float | |
| phosphorus: float | |
| potassium: float | |
| soil_moisture: float | |
| def get_latest_model(): | |
| try: | |
| api = HfApi() | |
| remote_info = api.model_info(repo_id=REPO_ID) | |
| remote_mtime = remote_info.lastModified | |
| cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) | |
| if os.path.exists(cached_path): | |
| local_mtime = datetime.fromtimestamp(os.path.getmtime(cached_path)) | |
| if datetime.now() - local_mtime < UPDATE_FREQUENCY: | |
| print("Using cached model (checked recently)") | |
| return joblib.load(cached_path) | |
| if remote_mtime > local_mtime: | |
| print("Downloading updated model") | |
| cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, force_download=True) | |
| else: | |
| print("Cached model is up-to-date") | |
| else: | |
| print("Downloading model for the first time") | |
| cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) | |
| except Exception as e: | |
| print(f"Error checking/downloading model: {e}") | |
| print(f"Error type: {type(e)}") | |
| print(f"Error details: {str(e)}") | |
| raise Exception("Unable to download or find the model.") | |
| return joblib.load(cached_path) | |
| def predict(crop_name, target_yield, field_size, ph, organic_carbon, nitrogen, phosphorus, potassium, soil_moisture): | |
| try: | |
| model = get_latest_model() | |
| input_data = { | |
| 'crop_name': [crop_name], | |
| 'target_yield': [target_yield], | |
| 'field_size': [field_size], | |
| 'ph': [ph], | |
| 'organic_carbon': [organic_carbon], | |
| 'nitrogen': [nitrogen], | |
| 'phosphorus': [phosphorus], | |
| 'potassium': [potassium], | |
| 'soil_moisture': [soil_moisture] | |
| } | |
| input_df = pd.DataFrame(input_data) | |
| prediction = model.predict(input_df) | |
| results = { | |
| 'nitrogen_need': float(prediction[0][0]), | |
| 'phosphorus_need': float(prediction[0][1]), | |
| 'potassium_need': float(prediction[0][2]), | |
| 'organic_matter_need': float(prediction[0][3]), | |
| 'lime_need': float(prediction[0][4]) | |
| } | |
| return results | |
| except Exception as e: | |
| return {"error": str(e)} | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Textbox(label="Crop Name"), | |
| gr.Number(label="Target Yield"), | |
| gr.Number(label="Field Size"), | |
| gr.Number(label="pH"), | |
| gr.Number(label="Organic Carbon"), | |
| gr.Number(label="Nitrogen"), | |
| gr.Number(label="Phosphorus"), | |
| gr.Number(label="Potassium"), | |
| gr.Number(label="Soil Moisture") | |
| ], | |
| outputs=gr.JSON(label="Prediction Results"), | |
| title="NPK Needs Prediction", | |
| description="Enter soil and crop details to predict NPK needs." | |
| ) | |
| iface.launch() |