Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load Pretrained Model and Tokenizer
+model_name = "text/sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define the Gradio Interface
+import easyocr
+def image_classifier(img):
+    model = YOLO('/content/drive/MyDrive/SIH_2023/YOLO/runs/detect/train/weights/best.pt')
+    res = model.predict(img,conf=0.25)
+    box = res[0].boxes.xywh[0]
+    bounding_box = box.cpu().numpy()
+    x0 = bounding_box[0] - bounding_box[2] / 2
+    x1 = bounding_box[0] + bounding_box[2] / 2
+    y0 = bounding_box[1] - bounding_box[3] / 2
+    y1 = bounding_box[1] + bounding_box[3] / 2
+
+    start_point = (int(x0), int(y0))
+    end_point = (int(x1), int(y1))
+    cv2.rectangle(img, start_point, end_point, color=(0,255,0), thickness=2)
+    # Use the easyocr reader for English language
+    reader = easyocr.Reader(['en'])
+
+    # Perform OCR on the input image
+    result = reader.readtext(img,allowlist="0123456789")
+
+    # Extract text and bounding box coordinates
+    text_and_coordinates = [(entry[1], entry[0]) for entry in result]
+    return text_and_coordinates
+
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs="text",
+    outputs="text",
+    live=True,
+    title="Sentiment Analysis",
+    description="Enter a sentence to predict sentiment.",
+)
+
+# Launch the Gradio Interface
+iface.launch()