Update app.py

app.py

@@ -42,87 +42,43 @@ def analyze_image(image, prompt):
     full_response = processor.decode(output[0])
     print("Full response:", full_response)  # Debug print
 
-    return full_response
-    # json_match = re.search(r'\{.*?\}', full_response, re.DOTALL)
-    # if json_match:
-    #     json_str = json_match.group(0)
-    #     try:
-    #         return json.loads(json_str)
-    #     except json.JSONDecodeError as e:
-    #         print(f"JSON decode error: {e}")
-    #         return {"error": "Failed to parse JSON", "full_response": full_response}
-    # else:
-    #     return {"error": "No JSON found in response", "full_response": full_response}
-
-# import torch
-# from PIL import Image
-# from transformers import AutoProcessor, AutoModelForCausalLM
-# import gradio as gr
-# import json
-# import traceback
-# import os
-
-# model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
-# token = os.getenv("HUGGINGFACE_TOKEN").strip()
-# processor = AutoProcessor.from_pretrained(model_name, token=token)
-# model = AutoModelForCausalLM.from_pretrained(
-#     model_name,
-#     quantization_config={"load_in_4bit": True},
-#     token=token
-# )
-
-# if torch.cuda.is_available():
-#     model = model.to('cuda')
-
-# def analyze_image(image, prompt):
-#     try:
-#         messages = [
-#             {"role": "user", "content": [
-#                 {"type": "image"},
-#                 {"type": "text", "text": prompt}
-#             ]}
-#         ]
-#         input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
-#         inputs = processor(
-#             image,
-#             input_text,
-#             add_special_tokens=False,
-#             return_tensors="pt"
-#         ).to(model.device)
-#         with torch.no_grad():
-#             output = model.generate(**inputs, max_new_tokens=100)
-#         result = processor.decode(output[0], skip_special_tokens=True)
-#         try:
-#             return json.loads(result)
-#         except json.JSONDecodeError:
-#             return {"error": "Failed to parse model output as JSON", "raw_output": result}
-#     except Exception as e:
-#         return {"error": str(e), "traceback": traceback.format_exc()}
-
-# default_prompt = """Analyze this image and determine if it contains a data logger. 
-# A data logger is typically a small, black electronic device used to monitor and record data 
-# over time, such as voltage, temperature, or current, via external sensors.
-
-# If a data logger is present in the image, respond with:
-# {"present": true, "reason": "Brief explanation of why you believe it's a data logger"}
-
-# If no data logger is visible, respond with:
-# {"present": false, "reason": "Brief explanation of why you believe there's no data logger"}
-
-# Ensure your response is in valid JSON format."""
-
-# iface = gr.Interface(
-#     fn=analyze_image,
-#     inputs=[
-#         gr.Image(type="pil", label="Upload Image"),
-#         gr.Textbox(label="Prompt", value=default_prompt, lines=10)
-#     ],
-#     outputs=gr.JSON(label="Analysis Result"),
-#     title="Data Logger Detection using Llama 3.2 Vision",
-#     description="Upload an image and customize the prompt to check if it contains a data logger.",
-#     examples=[
-#         ["bad.png", default_prompt]
-#     ]
-# )
+    # return full_response
+    try:
+        json_match = re.search(r'\{.*?\}', full_response, re.DOTALL)
+        if json_match:
+            json_str = json_match.group(0)
+            try:
+                return json.loads(json_str)
+            except json.JSONDecodeError as e:
+                return full_response
+    except Exception as e:
+        return full_response
+
+
+default_prompt = """Analyze this image and determine if it contains a data logger. 
+A data logger is typically a small, black electronic device used to monitor and record data 
+over time, such as voltage, temperature, or current, via external sensors.
+
+If a data logger is present in the image, respond with:
+{"present": true, "reason": "Brief explanation of why you believe it's a data logger"}
+
+If no data logger is visible, respond with:
+{"present": false, "reason": "Brief explanation of why you believe there's no data logger"}
+
+Ensure your response is in valid JSON format."""
+
+iface = gr.Interface(
+    fn=analyze_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Prompt", value=default_prompt, lines=10)
+    ],
+    outputs=gr.JSON(label="Analysis Result"),
+    title="Data Logger Detection using Llama 3.2 Vision",
+    description="Upload an image and customize the prompt to check if it contains a data logger.",
+    examples=[
+        ["bad.png", default_prompt]
+    ]
+)
 
-# iface.launch()
+iface.launch()