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| import os | |
| import tempfile | |
| from fastapi import FastAPI, UploadFile, File | |
| from fastapi.responses import HTMLResponse | |
| from transformers import AutoProcessor, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, Qwen2_5_VLForConditionalGeneration, AutoModel | |
| from qwen_vl_utils import process_vision_info | |
| # Specify the model path or identifier. | |
| MODEL_PATH = "Ananthu01/qwen2.5_vl_finetuned_model" | |
| # Initialize the Qwen2.5 VL model. | |
| model = Qwen2_5_VLForConditionalGeneration.from_pretrained( | |
| MODEL_PATH, | |
| trust_remote_code=True, | |
| device_map="cpu", # Ensures the model is loaded on CPU. | |
| use_safetensors=True | |
| ) | |
| # Load the processor. | |
| processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", trust_remote_code=True) | |
| # Load the tokenizer. | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) | |
| # Define generation parameters. | |
| generation_config = GenerationConfig( | |
| temperature=0.1, # Adjust temperature as needed. | |
| top_p=0.8, # Nucleus sampling probability. | |
| repetition_penalty=1.05, # Penalty to avoid repetitive outputs. | |
| max_new_tokens=1024 # Maximum tokens to generate. | |
| ) | |
| # Create FastAPI app instance. | |
| app = FastAPI() | |
| async def main(): | |
| """ | |
| GET endpoint that renders an HTML form for the user to upload an image. | |
| """ | |
| content = """ | |
| <html> | |
| <head> | |
| <title>Qwen2.5 VL Image Upload</title> | |
| </head> | |
| <body> | |
| <h2>Upload an Image</h2> | |
| <form action="/generate" enctype="multipart/form-data" method="post"> | |
| <input name="image_file" type="file" accept="image/*"> | |
| <input type="submit" value="Submit"> | |
| </form> | |
| </body> | |
| </html> | |
| """ | |
| return content | |
| async def generate_output(image_file: UploadFile = File(...)): | |
| """ | |
| POST endpoint to generate model output using an uploaded image. | |
| The text prompt is fixed to "Extract JSON". | |
| - **image_file**: The image file uploaded by the user. | |
| """ | |
| # Read the uploaded image. | |
| image_bytes = await image_file.read() | |
| # Save the image temporarily. | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp: | |
| tmp.write(image_bytes) | |
| tmp_path = tmp.name | |
| # Construct messages with a hardcoded text instruction. | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "image", "image": tmp_path}, | |
| {"type": "text", "text": "Extract JSON"}, | |
| ], | |
| } | |
| ] | |
| # Apply the chat template using the processor. | |
| prompt = processor.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| # Process multimodal inputs. | |
| image_inputs, video_inputs = process_vision_info(messages) | |
| mm_data = {} | |
| if image_inputs is not None: | |
| mm_data["image"] = image_inputs | |
| # Tokenize the prompt. | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| inputs = {k: v.to("cpu") for k, v in inputs.items()} | |
| # Generate output from the model. | |
| # Note: It is assumed that Qwen2.5 VL’s generate method accepts a multi_modal_data argument. | |
| generated_ids = model.generate( | |
| **inputs, | |
| generation_config=generation_config, | |
| multi_modal_data=mm_data | |
| ) | |
| # Decode the generated text. | |
| generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) | |
| # Clean up the temporary file. | |
| os.remove(tmp_path) | |
| # Return the generated text. | |
| return generated_text | |