Update app.py

app.py

@@ -1,71 +1,53 @@
 import gradio as gr
 import json
-import json5
+from transformers import pipeline
 
-# Function to attempt to fix JSON using json5
-def fix_json(json_data):
-    try:
-        # First, try parsing with json5 which is more lenient
-        parsed_data = json5.loads(json_data)
-
-        # Try to pretty-print (indent) the parsed data
-        fixed_json = json.dumps(parsed_data, indent=4)
-        return fixed_json, "JSON fixed using json5."
-
-    except Exception as e:
-        return None, f"Failed to fix JSON: {str(e)}"
+# Load a text generation model from Hugging Face without an API key
+# You can use a smaller model if gpt-3.5-turbo isn't available
+generator = pipeline("text-generation", model="gpt-3.5-turbo")
 
-# Function to remove duplicates and fix JSON
-def process_json(json_data):
-    # Check if the uploaded JSON data is empty
-    if not json_data.strip():
-        return None, "Error: The uploaded file is empty.", None
+# Function to attempt to fix JSON using a Hugging Face model
+def ai_fix_json(json_data):
+    # Prepare a prompt for the AI model to fix the JSON
+    prompt = f"Fix the following JSON data and make it valid:\n\n{json_data}\n\nFixed JSON:"
 
-    # Attempt to fix the JSON
-    fixed_json, fix_message = fix_json(json_data)
-    if fixed_json is None:
-        return None, fix_message, None
+    # Generate a response from the model
+    response = generator(prompt, max_length=1024, num_return_sequences=1)[0]['generated_text']
 
+    # Extract the fixed JSON from the response (you may need to fine-tune this depending on the model's output format)
+    fixed_json = response.split("Fixed JSON:")[-1].strip()
+    
+    # Try to load the fixed JSON to ensure it's valid
     try:
-        # Parse the fixed JSON string to a list of dictionaries
-        data = json.loads(fixed_json)
-
-        # Check if data is a list of dictionaries
-        if not isinstance(data, list):
-            return None, "Error: JSON is not a list of objects", None
-
-        # Remove duplicates by converting the list to a set of tuples, then back to a list of dicts
-        unique_data = [dict(t) for t in {tuple(d.items()) for d in data}]
-
-        # Convert the list back to JSON
-        cleaned_json = json.dumps(unique_data, indent=4)
-
-        return cleaned_json, f"JSON is valid and cleaned. {fix_message}", cleaned_json
-
+        parsed_data = json.loads(fixed_json)
+        pretty_json = json.dumps(parsed_data, indent=4)
+        return pretty_json, "JSON fixed using AI."
+    
     except json.JSONDecodeError as e:
-        return None, f"Invalid JSON after fixing: {e}", None
+        return None, f"Failed to fix JSON: {str(e)}"
 
 # Function to handle file uploads and processing
 def process_file(uploaded_file):
     # Read the content of the uploaded file directly
     json_data = uploaded_file  # This is already the content of the file as a string
-
-    # Process the JSON data
-    cleaned_json, message, download_json = process_json(json_data)
-
-    if message:
-        return cleaned_json, message, download_json
-
-    # Return the cleaned JSON for display and as a downloadable file
-    return cleaned_json, "JSON is valid and cleaned.", download_json
+    
+    # Attempt to fix the JSON using AI
+    cleaned_json, message = ai_fix_json(json_data)
+    
+    if cleaned_json:
+        # Return the fixed JSON for display and as a downloadable file
+        return cleaned_json, message, cleaned_json
+    
+    else:
+        return None, message, None
 
 # Gradio interface
 iface = gr.Interface(
     fn=process_file,
     inputs=gr.File(label="Upload your JSON file"),
     outputs=[gr.JSON(label="Fixed JSON"), "text", gr.File(label="Download cleaned JSON file")],
-    title="Advanced JSON Cleaner and Fixer",
-    description="Upload a JSON file to automatically fix, remove duplicates, and download the cleaned version."
+    title="AI-Powered JSON Cleaner",
+    description="Upload a JSON file to automatically fix, remove duplicates, and download the cleaned version using AI."
 )
 
 iface.launch()