Update app.py

app.py

@@ -1,66 +1,24 @@
 import gradio as gr
-import torch
-import pytesseract
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# Load the Hugging Face model for object detection
-model_name = "flax-community/yolov5s-v1-coco"
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
-
-# Load the Hugging Face model for text classification
-classification_model_name = "distilbert-base-uncased"
-classification_tokenizer = AutoTokenizer.from_pretrained(classification_model_name)
-classification_model = AutoModelForSequenceClassification.from_pretrained(classification_model_name)
-
-# Define function for text recognition (OCR)
-def perform_ocr(image):
-    # Perform OCR using pytesseract
-    text = pytesseract.image_to_string(image)
-    return text
-
-# Define function to process video and predict
-def predict_runner_status(video_file):
-    cap = cv2.VideoCapture(video_file.name)
-
-    results = []
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Object detection
-        results_detection = model(frame)
-
-        # Logic for determining runner status using detected objects
-        objects = results_detection.pred[0][:, -1].numpy()
-        if 0 in objects:  # 0 corresponds to person class
-            # Perform OCR on the detected person
-            person_bbox = results_detection.pred[0][np.where(objects == 0)][0][:4]
-            person_bbox = person_bbox.astype(int)
-            person_img = frame[person_bbox[1]:person_bbox[3], person_bbox[0]:person_bbox[2]]
-            text = perform_ocr(person_img)
-
-            # Classification using text classification model
-            inputs_classification = classification_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-            outputs_classification = classification_model(**inputs_classification)
-            predicted_class = torch.argmax(outputs_classification.logits).item()
-            if predicted_class == 1:
-                runner_status = "Out"
-            else:
-                runner_status = "Safe"
-
-            result = {
-                "frame_number": cap.get(cv2.CAP_PROP_POS_FRAMES),
-                "runner_status": runner_status
-            }
-            results.append(result)
-
-    cap.release()
-
-    return results
-
-inputs = gr.inputs.Video(type="file", label="Upload a baseball video")
-outputs = gr.outputs.Label(type="auto", label="Runner Status")
-interface = gr.Interface(fn=predict_runner_status, inputs=inputs, outputs=outputs, title="Baseball Runner Status Predictor")
-interface.launch(share=True)
+from transformers import pipeline
+
+# Replace with a suitable image classification model ID
+model_id = "sayakpaul/resnet-50-finetuned-imagenet"
+
+def analyze_image(image):
+    classifier = pipeline("image-classification", model=model_id)
+    predictions = classifier(images=image)  # Assuming the model outputs probabilities
+    # Extract the most likely class and its probability
+    top_class = predictions[0]["label"]
+    top_prob = predictions[0]["score"]
+    return f"Top Class: {top_class} (Probability: {top_prob:.2f})"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=analyze_image,
+    inputs="image",
+    outputs="text",
+    title="Image Analyzer (Generic)",
+    description="Upload an image and get the most likely classification based on the chosen model.",
+)
+
+interface.launch()