V0.1

Dockerfile

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+# Stage 1: Use an official Python runtime as a parent image
+# Using a -slim version is a good practice as it reduces the final image size.
+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt ./
+
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.headless=true"]

app.py

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+import streamlit as st
+import os
+import json
+from openai import OpenAI
+from dotenv import load_dotenv
+import re
+
+load_dotenv()
+
+client = OpenAI(
+  base_url="https://openrouter.ai/api/v1",
+  api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+def generate_code_analysis_with_retry(code_snippet: str, model_name: str,language: str, max_retries: int = 3):
+    """
+    Generates code analysis with a self-correction loop.
+    It tries to get valid JSON, and if it fails, it tells the AI its mistake and retries.
+    Returns a parsed dictionary on success, or None on failure.
+    """
+    
+    # Define the initial user request
+    initial_prompt = f"""
+    You are an expert {language} programmer. Analyze the following code snippet and provide a plain-English explanation and a Google-style docstring.
+    
+    Code:
+    ```
+    {code_snippet}
+    ```
+    
+    Respond with ONLY a single, valid JSON object with two keys: "explanation" and "docstring". Do not include any markdown formatting, comments, or other text outside of the JSON.
+    """
+    
+    # Initialize the conversation history for the AI
+    messages = [{"role": "user", "content": initial_prompt}]
+    
+    # Start the self-correction loop
+    for attempt in range(max_retries):
+        st.write(f" Attempt {attempt + 1} of {max_retries}...") 
+        
+        try:
+            # === ACT: Call the AI ===
+            response = client.chat.completions.create(
+                model=model_name,
+                messages=messages
+            )
+            raw_output = response.choices[0].message.content
+            
+            
+            # Use regex to find the JSON object within the potentially messy string
+            match = re.search(r"\{.*\}", raw_output, re.DOTALL)
+            if not match:
+                raise ValueError("No JSON object found in the response.")
+            
+            cleaned_json_str = match.group(0)
+            
+            parsed_json = json.loads(cleaned_json_str)
+            
+            if "explanation" not in parsed_json or "docstring" not in parsed_json:
+                raise ValueError("JSON is missing required keys ('explanation', 'docstring').")
+
+            st.success(f"Analysis successful on attempt {attempt + 1}!")
+            return parsed_json 
+
+        except (json.JSONDecodeError, ValueError, IndexError) as e:
+            # === REASON & REACT: If an error occurred, start the correction process ===
+            st.warning(f"Attempt {attempt + 1} failed: {e}. Trying to self-correct...")
+            
+            # Add the AI's failed response to the conversation history
+            messages.append({"role": "assistant", "content": raw_output})
+            
+            # Create the corrective prompt, showing the AI its own mistake
+            corrective_prompt = f"""
+            Your previous response could not be parsed.
+            Error: "{e}"
+            Your full response was:
+            ---
+            {raw_output}
+            ---
+            Please correct your mistake. Look at the error and your previous response. 
+            Provide the response again as a single, valid JSON object with the keys "explanation" and "docstring". 
+            DO NOT wrap it in markdown or add any other text.
+            """
+            
+            # Add  corrective instruction to the conversation
+            messages.append({"role": "user", "content": corrective_prompt})
+            
+    st.error(f"Failed to get a valid response after {max_retries} attempts.")
+    return None
+
+
+
+st.set_page_config(layout="wide")
+st.title("AI Code Explainer & Docstring Generator")
+st.write("Powered by OpenRouter.ai with a Self-Correction Loop")
+
+code_input = st.text_area(
+    "Paste your Python function or code block here:", 
+    height=250, 
+    placeholder="def my_function(arg1, arg2):\n    # Your code here\n    return result"
+)
+
+model_choice = st.selectbox(
+    "Choose your AI model:",
+    (
+        "Google: Gemma 3n",
+        "MoonshotAI: Kimi Dev ",
+        "NVIDIA: Nemotron Nano 9B",
+        "Mistral: Mistral 7B Instruct",
+    ),
+    help="Free models from OpenRouter. Different models have different strengths."
+)
+
+MODEL_MAPPING = {
+    "Google: Gemma 3n": "google/gemma-3n-e2b-it:free",
+    "MoonshotAI: Kimi Dev ": "moonshotai/kimi-dev-72b:free",
+    "NVIDIA: Nemotron Nano 9B": "nvidia/nemotron-nano-9b-v2:free",
+    "Mistral: Mistral 7B Instruct": "mistralai/mistral-7b-instruct:free",
+}
+selected_model_id = MODEL_MAPPING[model_choice]
+
+language = st.selectbox("Select Language", ["Python", "JavaScript", "Java", "Go"])
+if st.button("Analyze Code", type="primary"):
+    if code_input:
+        analysis_dict = generate_code_analysis_with_retry(code_input, selected_model_id,language)
+        
+        if analysis_dict:
+            st.subheader("Final Analysis Results")
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.info("💬 Plain English Explanation")
+                st.write(analysis_dict.get("explanation", "No explanation was generated."))
+            
+            with col2:
+                st.success("📝 Generated Docstring")
+                st.code(analysis_dict.get("docstring", "No docstring was generated."), language="python")
+    else:
+        st.warning("Please paste some code into the text area above.")

requirements.txt

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+openai
+dotenv
+streamlit