Add application file

qwen_gradio.py

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+import gradio as gr
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+# Specify the local cache path for models
+local_path = "/root/.cache/huggingface/hub/models--Qwen--Qwen2-VL-7B-Instruct/snapshots/a28a094eb66a9f2ac70eef346f040d8a79977472"
+
+# Load model and processor
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    local_path, torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained(local_path)
+
+# Function to process image and text and generate the output
+def generate_output(image, text, button_click):
+    # Prepare input data
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": text},
+            ],
+        }
+    ]
+    
+    # Prepare inputs for the model
+    text_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text_input],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to("cuda")
+
+    # Generate the output
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    return output_text[0]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_output,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(lines=2, placeholder="Enter a question related to the image", label="Input Text"),
+        
+    ],
+    outputs=gr.Textbox(label="Model Output"),
+)
+
+# Launch the Gradio interface
+iface.launch()