Atchyuteswar/sih

1import tensorflow as tf
2import cv2
3import nltk
4from nltk.tokenize import word_tokenize
5from nltk.stem import PorterStemmer
6import gradio as gr
7
8# Load pre-trained models for object detection and natural language processing
9
10def detect_objects(image_path):
11    # Use OpenCV and TensorFlow to detect objects in the image
12    objects = ...
13    return objects
14
15def classify_intent(query):
16    # Use NLTK or spaCy to tokenize and preprocess the query
17    tokens = word_tokenize(query)
18    stemmer = PorterStemmer()
19    stemmed_tokens = [stemmer.stem(token) for token in tokens]
20
21    # Use a trained model to classify the intent
22    intent = ...
23    return intent
24
25def generate_response(intent, objects):
26    # Use a trained model to generate a response based on the intent and objects
27    response = ...
28    return response
29
30# Main function
31def chatbot(image_path, query):
32    objects = detect_objects(image_path)
33    intent = classify_intent(query)
34    response = generate_response(intent, objects)
35    return response
36
37# Gradio interface
38def chatbot_interface(image, query):
39    response = chatbot(image, query)
40    return response
41
42# Create a Gradio interface
43iface = gr.Interface(
44    fn=chatbot_interface,
45    inputs=[
46        gr.Image(label="Image", type="file"),
47        gr.Textbox(label="Query"),
48    ],
49    outputs="text",
50    title="Image Recognition Chatbot"
51)
52
53iface.launch()