Initial commit for deployment

Dockerfile

@@ -1,17 +0,0 @@
-# Use Python as the base image
-FROM python:3.9
-
-# Set working directory
-WORKDIR /app
-
-# Copy project files
-COPY . /app
-
-# Install dependencies
-RUN pip install flask torch transformers
-
-# Expose the API port
-EXPOSE 5000
-
-# Start Flask server
-CMD ["python", "app.py"]

app.py

@@ -1,52 +1,34 @@
-import gradio as gr
-from bug_detector import fix_code
-
-def run_bugfixer(code):
-    return fix_code(code)
-
-gr.Interface(
-    fn=run_bugfixer,
-    inputs=gr.Textbox(label="Paste Buggy Code", lines=15),
-    outputs=gr.Textbox(label="Suggested Fixed Code"),
-    title="Automated BUg Fixing and Dection"
-).launch()
-from flask import Flask, request, jsonify
-from flask_cors import CORS  # Fix CORS issue
+ifrom flask import Flask, request, jsonify
+from flask_cors import CORS
 import torch
 from transformers import RobertaTokenizer, RobertaForSequenceClassification
 
 app = Flask(__name__)
-CORS(app)  # Enable CORS
+CORS(app)
 
-# Load CodeBERT model
 tokenizer = RobertaTokenizer.from_pretrained("microsoft/codebert-base")
 model = RobertaForSequenceClassification.from_pretrained("microsoft/codebert-base")
 
 @app.route("/")
 def home():
-    return "Bug Detection and Fixing API is running!"
+    return "✅ Bug Detection and Fixing API is Running!"
 
 @app.route("/detect", methods=["POST"])
 def detect_bug():
     try:
         data = request.get_json()
         code = data.get("code", "")
-
         if not code:
             return jsonify({"error": "No code provided"}), 400
 
-        # Tokenize and classify
         inputs = tokenizer(code, return_tensors="pt", truncation=True, padding=True)
         outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
         prediction = torch.argmax(outputs.logits, dim=1).item()
+        status = "🐛 Buggy" if prediction == 1 else "✅ Clean"
+        return jsonify({"status": status})
 
-        bug_status = "buggy" if prediction == 1 else "clean"
-
-        return jsonify({"status": bug_status})
-    
     except Exception as e:
-        return jsonify({"error": str(e)}), 500  # Handle errors properly
+        return jsonify({"error": str(e)}), 500
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=5000)  # Ensure compatibility with Docker
-
+    app.run(host="0.0.0.0", port=5000)

app_ui.py

@@ -1,18 +1,22 @@
 import gradio as gr
-from full_code_fixer import fix_code
+from bug_detector import fix_code
 
-def fix_script(code):
+def run_bugfixer(code):
     fixed = fix_code(code)
-    return "Analyzed", fixed
+    return fixed
 
-with gr.Blocks() as demo:
-    gr.Markdown("## 🛠️ Bug Detection and Fixing Tool - Full Script Version")
-    code_input = gr.Textbox(lines=20, label="Paste Your Python Code Here")
-    bug_status = gr.Textbox(label="Status")
-    fixed_code = gr.Textbox(lines=20, label="Suggested Fix")
-
-    gr.Button("Detect and Fix").click(fix_script, inputs=code_input, outputs=[bug_status, fixed_code])
-
-demo.launch()
-
-#for the gradio user interface
+gr.Interface(
+    fn=run_bugfixer,
+    inputs=gr.Textbox(label="💥 Paste Buggy Code", lines=15),
+    outputs=gr.Textbox(label="🛠️ Suggested Fixed Code", lines=15, interactive=True),  # ✅ EDITABLE OUTPUT
+    title="🚀 Automated Bug Fixing and Detection",
+    description="""
+    <h3 style='color: orange;'>Developed by:</h3>
+    <ul>
+        <li><strong>Akanksha K P</strong></li>
+        <li><strong>Divyashree</strong></li>
+    </ul>
+    <h4 style='color: cyan;'>Guide: Prof. Alakananda K P</h4>
+    """,
+    theme="soft"
+).launch()

bug_detector.py

@@ -1,14 +1,13 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
 
-# Load CodeT5 or similar model
 model_name = "Salesforce/codet5-base"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-def fix_code(code: str) -> str:
-    input_text = f"fix: {code}"
-    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+def fix_code(code):
+    prompt = f"fix Python: {code}"
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
     with torch.no_grad():
-        output = model.generate(**inputs, max_length=512)
+        output = model.generate(**inputs, max_length=512, num_beams=4, early_stopping=True)
     return tokenizer.decode(output[0], skip_special_tokens=True)