Initial commit

app.py

@@ -0,0 +1,37 @@
+import streamlit as st
+import os
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# UI Components for Streamlit
+st.title("Ansible Code Reviewer")
+uploaded_files = st.file_uploader("Upload your Ansible code files", type=['yml', 'yaml'], accept_multiple_files=True)
+
+if uploaded_files:
+    result = []
+    model_name = "facebook/incoder-1B"  # Example model name
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+
+    # Process each uploaded file
+    for uploaded_file in uploaded_files:
+        content = uploaded_file.read().decode("utf-8")
+        # Here you could use the model to evaluate the content
+        tokens = tokenizer(content, return_tensors="pt")
+        review_output = model.generate(**tokens)
+        review_text = tokenizer.decode(review_output[0], skip_special_tokens=True)
+
+        # Store results
+        result.append({
+            "filename": uploaded_file.name,
+            "review": review_text
+        })
+
+    # Save the results to a JSON file
+    json_result = json.dumps(result, indent=4)
+    with open("review_results.json", "w") as f:
+        f.write(json_result)
+
+    # Display results and download link
+    st.json(result)
+    st.download_button("Download Review Results", json_result, file_name="review_results.json")

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+transformers
+ansible
+pandas