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6c463a9 77eb764 6c463a9 77eb764 0b8bbf7 77eb764 0b8bbf7 6c463a9 77eb764 20e58ac 77eb764 a75ac77 6c463a9 77eb764 0b8bbf7 a75ac77 77eb764 20e58ac 77eb764 20e58ac a75ac77 20e58ac 77eb764 20e58ac 77eb764 20e58ac 77eb764 6c463a9 77eb764 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | import gradio as gr
import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow.keras.preprocessing import image
import os
# Check if the model file exists before loading
MODEL_PATH = "civil_tool_classifier.h5"
if not os.path.exists(MODEL_PATH):
print(f"Error: Model file '{MODEL_PATH}' not found.")
model = None
else:
try:
model = tf.keras.models.load_model(MODEL_PATH)
except Exception as e:
print(f"Error loading model: {e}")
model = None
# Check if the CSV file exists before loading
CSV_PATH = "tools.csv"
if not os.path.exists(CSV_PATH):
print(f"Error: CSV file '{CSV_PATH}' not found.")
tools_df = None
CLASS_NAMES = []
else:
try:
tools_df = pd.read_csv(CSV_PATH)
CLASS_NAMES = list(tools_df["tool_name"].values)
except Exception as e:
print(f"Error loading CSV file: {e}")
tools_df = None
CLASS_NAMES = []
def predict_tool(img):
if model is None or tools_df is None:
return {"tool": "Error", "usage": "Model/CSV not loaded", "safety": "N/A"}
# Pre-process the image
img = image.array_to_img(img).resize((224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0
# Predict
predictions = model.predict(img_array)
class_idx = np.argmax(predictions[0])
# Get the tool info from the DataFrame
if class_idx < len(CLASS_NAMES):
tool_name = CLASS_NAMES[class_idx]
try:
row = tools_df[tools_df["tool_name"] == tool_name].iloc[0]
usage = row.get("tool_usage", "Usage not available.")
safety = row.get("tool_safety", "Safety not available.")
except Exception:
usage = "Usage information not available."
safety = "Safety guidance not available."
else:
tool_name = "Unknown Tool"
usage = "Not found in the list."
safety = "Not found in the list."
# 👇 Return JSON instead of multiple textboxes
return {"tool": tool_name, "usage": usage, "safety": safety}
# Gradio interface with JSON output
demo = gr.Interface(
fn=predict_tool,
inputs=gr.Image(type="numpy"),
outputs=gr.JSON(), # 👈 JSON output for API
title="Civil Tool Classifier",
description="Upload an image of a civil tool to get its details."
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
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