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Dockerfile

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+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt requirements.txt
+COPY app.py app.py
+COPY README.md README.md
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose default Streamlit port
+EXPOSE 8501
+
+# Command to run the app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.enableCORS=false"]

README.md

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 ---
 title: Programming Help Chatbot
-emoji: 🐢
-colorFrom: green
-colorTo: pink
-sdk: docker
+emoji: 💻
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: "1.20.0"
+app_file: app.py
 pinned: false
-license: mit
-short_description: programming chatbot
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+---
+
+# Programming Help Chatbot 🤖
+
+A Minimal Viable Product (MVP) AI chatbot built as part of the **AML-3304** assignment:  
+**"From Tokens to Transformers – Building AI Systems with Embeddings, Generative Models, and MLOps."**
+
+---
+
+## 🔍 Project Overview
+
+This chatbot is designed to assist with programming-related queries. It leverages a pretrained generative transformer model to provide real-time code suggestions in response to natural language inputs. This is not a rule-based system, but a lightweight generative AI application hosted on Hugging Face Spaces.
+
+- **Model:** `Salesforce/codegen-350M-mono` (Pretrained by Salesforce Research)
+- **Frontend:** Streamlit
+- **Framework:** Hugging Face Transformers + PyTorch
+- **Hosting:** Hugging Face Spaces (Free CPU Tier)
+- **Deployment:** Manual Git-based CI/CD with automated runtime build
+
+---
+
+## ✨ Features
+
+- Accepts open-ended natural language programming questions
+- Generates accurate code snippets (Python, JavaScript, etc.)
+- Intuitive UI powered by Streamlit
+- Minimal resource usage (compatible with Hugging Face Free Tier)
+
+---
+
+## 🧠 AI Design Elements
+
+| Element             | Integration                                         |
+|---------------------|-----------------------------------------------------|
+| Embeddings          | Handled internally via pretrained model tokenizer   |
+| Bayesian-style sampling | `do_sample=True` with temperature in generation    |
+| Generative Language Model | `Salesforce/codegen-350M-mono` (Causal LM)      |
+| PyTorch Backend     | Used via `AutoModelForCausalLM`                     |
+
+---
+
+## 🛠️ Technologies Used
+
+- Python 3.9+
+- Streamlit
+- Hugging Face Transformers
+- PyTorch
+- Hugging Face Spaces
+
+---
+
+## 🚀 Usage Instructions
+
+1. Visit the live space:  
+   👉 `https://huggingface.co/spaces/YOUR_USERNAME/programming-help-chatbot`
+
+2. Type your question into the input box, e.g.:
+   ```
+   Write a Python function to check if a number is a prime.
+   ```
+
+3. Press “Generate Code” and view the results in a highlighted code block.
+
+---
+
+## 📦 Installation (For Local Testing)
+
+```bash
+pip install streamlit transformers torch
+streamlit run app.py
+```
+
+---
+
+## 📜 License
+
+This project is licensed under the MIT License.
+
+---
+
+## 👤 Author
+
+Created by **[Your Name]**  
+Part of the AML-3304 Course Project Submission

app.py

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+# app.py
+# Programming Help Chatbot – AI Chatbot MVP (AML-3304)
+# Built using Streamlit and Hugging Face Transformers
+# Model: Salesforce/codegen-350M-mono
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Set Streamlit page configuration
+st.set_page_config(page_title="Programming Help Chatbot", layout="centered")
+
+# Page Title and Description
+st.title("👨‍💻 Programming Help Chatbot")
+st.markdown("""
+Welcome to the AI-powered programming assistant!  
+This chatbot uses a pretrained transformer model to generate helpful code snippets for Python, JavaScript, and more.
+""")
+
+# Load pretrained code generation model and tokenizer
+@st.cache_resource
+def load_codegen_model():
+    """
+    Load the Salesforce/codegen-350M-mono model and tokenizer.
+    Cached to avoid reloading on every interaction.
+    """
+    model_name = "Salesforce/codegen-350M-mono"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+# Initialize model and tokenizer
+tokenizer, model = load_codegen_model()
+
+# User input prompt
+user_query = st.text_area(
+    label="📝 Enter your programming question:",
+    height=150,
+    placeholder="Example: Write a Python function to check for palindrome strings"
+)
+
+# Generate code on button click
+if st.button("Generate Code"):
+    if user_query.strip():
+        # Encode input and generate code
+        inputs = tokenizer(user_query, return_tensors="pt")
+        outputs = model.generate(
+            inputs["input_ids"],
+            max_length=256,
+            do_sample=True,
+            temperature=0.7,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Display result
+        st.subheader("💡 Suggested Code:")
+        st.code(generated_code, language="python")
+    else:
+        st.warning("Please enter a valid programming question.")

requirements.txt

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+streamlit>=1.20.0
+transformers>=4.36.0
+torch>=2.0.0