#!/usr/bin/env python3


# Store a DNA sequence in a variable
dna = "ACGGGAGGACGGGAAAATTACTACGGCATTAGC"

# Print it
print(dna)

# A few things you can do with a string
print(f"Length: {len(dna)}")
print(f"First base: {dna[0]}")
print(f"Last base: {dna[-1]}")
print(f"First 10 bases: {dna[:10]}")

#!/usr/bin/env python3


import os

# Try to import the AI client -- works with OpenAI-compatible APIs
try:
    from openai import OpenAI
    client = OpenAI(
        api_key=os.environ.get("OPENAI_API_KEY", ""),
        base_url=os.environ.get("OPENAI_BASE_URL", "https://openrouter.ai/api/v1"),
    )
    AI_AVAILABLE = True
except ImportError:
    AI_AVAILABLE = False
    print("Note: Install openai package for AI features (pip install openai)")
    print("Running in offline mode.\n")


def ask_ai(prompt: str) -> str:
    """Send a prompt to the LLM and return the response."""
    if not AI_AVAILABLE:
        return "(AI not available -- set OPENAI_API_KEY environment variable)"
    response = client.chat.completions.create(
        model=os.environ.get("OPENAI_MODEL", "google/gemini-2.5-flash"),
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3,
    )
    return response.choices[0].message.content


# --- Same code as the vanilla version ---
dna = "ACGGGAGGACGGGAAAATTACTACGGCATTAGC"
print(dna)
print(f"Length: {len(dna)}")
print(f"First base: {dna[0]}")
print(f"Last base: {dna[-1]}")
print(f"First 10 bases: {dna[:10]}")

# --- Now add AI analysis ---
print("\n--- AI Analysis ---\n")

# Ask the LLM to analyze the sequence
result = ask_ai(
    f"I have a DNA sequence: {dna}\n\n"
    "Please tell me:\n"
    "1. What is the GC content (percentage of G and C bases)?\n"
    "2. Is this likely a coding sequence or random DNA? Why?\n"
    "3. What organism might this come from?\n\n"
    "Keep your answer brief and suitable for a college freshman."
)
print(result)

# Ask for a biological interpretation
print("\n--- Biological Context ---\n")
result = ask_ai(
    f"Given this DNA sequence: {dna}\n\n"
    "If this were part of a gene, what kinds of proteins might it help encode? "
    "Explain in one paragraph, using language a non-biology major would understand."
)
print(result)

#!/usr/bin/env python3


# Two DNA fragments
dna1 = "ACGGGAGGACGGGAAAATTACTACGGCATTAGC"
dna2 = "ATAGTGCCGTGAGAGTGATGTAGTA"

print("Here are the original two DNA fragments:\n")
print(dna1)
print(dna2)

# Concatenate them
combined = dna1 + dna2
print(f"\nConcatenated DNA ({len(combined)} bases):")
print(combined)

# You can also use f-strings
print(f"\nFragment 1 is {len(dna1)} bases, fragment 2 is {len(dna2)} bases")
print(f"Combined length: {len(dna1) + len(dna2)} bases")

# Insert a spacer
spacer = "NNNNN"
with_spacer = dna1 + spacer + dna2
print(f"\nWith spacer '{spacer}':")
print(with_spacer)

#!/usr/bin/env python3


import os

try:
    from openai import OpenAI
    client = OpenAI(
        api_key=os.environ.get("OPENAI_API_KEY", ""),
        base_url=os.environ.get("OPENAI_BASE_URL", "https://openrouter.ai/api/v1"),
    )
    AI_AVAILABLE = True
except ImportError:
    AI_AVAILABLE = False
    print("Note: Install openai package for AI features (pip install openai)\n")


def ask_ai(prompt: str) -> str:
    if not AI_AVAILABLE:
        return "(AI not available -- set OPENAI_API_KEY environment variable)"
    response = client.chat.completions.create(
        model=os.environ.get("OPENAI_MODEL", "google/gemini-2.5-flash"),
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3,
    )
    return response.choices[0].message.content


dna1 = "ACGGGAGGACGGGAAAATTACTACGGCATTAGC"
dna2 = "ATAGTGCCGTGAGAGTGATGTAGTA"

print("Fragment 1:", dna1)
print("Fragment 2:", dna2)

combined = dna1 + dna2
print(f"\nConcatenated ({len(combined)} bases): {combined}")

# --- AI: Analyze the junction ---
print("\n--- AI: What happens at the junction? ---\n")

result = ask_ai(
    f"I concatenated two DNA fragments:\n"
    f"Fragment 1 (ends with): ...{dna1[-10:]}\n"
    f"Fragment 2 (starts with): {dna2[:10]}...\n"
    f"Combined: {combined}\n\n"
    "In molecular biology, when scientists join two DNA fragments:\n"
    "1. What is this process called? (Hint: think restriction enzymes and ligase)\n"
    "2. The junction between the two fragments -- does it create a new sequence "
    "that wasn't in either original fragment?\n"
    "3. Could this new junction accidentally create a start codon (ATG) "
    "or stop codon (TAA, TAG, TGA)?\n\n"
    "Explain for a college freshman."
)
print(result)

# --- AI: GC content comparison ---
print("\n--- AI: Comparing the fragments ---\n")

gc1 = (dna1.count("G") + dna1.count("C")) / len(dna1) * 100
gc2 = (dna2.count("G") + dna2.count("C")) / len(dna2) * 100

result = ask_ai(
    f"Two DNA fragments were concatenated:\n"
    f"Fragment 1 ({len(dna1)} bases, GC={gc1:.1f}%): {dna1}\n"
    f"Fragment 2 ({len(dna2)} bases, GC={gc2:.1f}%): {dna2}\n\n"
    "The GC content differs between the fragments. What does this tell us?\n"
    "1. Could these fragments come from different organisms?\n"
    "2. What is 'GC content' and why do scientists care about it?\n"
    "3. How might different GC content affect DNA stability?\n\n"
    "Keep it brief."
)
print(result)

#!/usr/bin/env python3


codon = "ATG"

# Check if this is a start codon
if codon == "ATG":
    print(f"{codon} is the universal start codon (codes for Methionine)")
elif codon in ("TAA", "TAG", "TGA"):
    print(f"{codon} is a stop codon (translation ends here)")
else:
    print(f"{codon} is a regular codon")

# Check a sequence for start and stop codons
dna = "ATGGCCTGAACCGATCGATCG"
print(f"\nAnalyzing: {dna}")
print(f"First 3 bases: {dna[:3]}")
print(f"Last 3 bases:  {dna[-3:]}")

if dna[:3] == "ATG":
    print("  -> Starts with ATG (start codon)")
else:
    print(f"  -> Starts with {dna[:3]} (not a start codon)")

if dna[-3:] in ("TAA", "TAG", "TGA"):
    print(f"  -> Ends with {dna[-3:]} (stop codon)")
else:
    print(f"  -> Ends with {dna[-3:]} (not a stop codon)")

# Categorize multiple codons
print("\nCodon classification:")
codons = ["ATG", "TAA", "GCT", "TGA", "TAG", "ATC", "TTT"]
for c in codons:
    if c == "ATG":
        category = "START"
    elif c in ("TAA", "TAG", "TGA"):
        category = "STOP"
    else:
        category = "coding"
    print(f"  {c}: {category}")

#!/usr/bin/env python3


import os

try:
    from openai import OpenAI
    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", ""), base_url=os.environ.get("OPENAI_BASE_URL", "https://openrouter.ai/api/v1"))
    AI_AVAILABLE = True
except ImportError:
    AI_AVAILABLE = False
    print("Note: Install openai package for AI features (pip install openai)\n")

def ask_ai(prompt: str) -> str:
    if not AI_AVAILABLE:
        return "(AI not available)"
    response = client.chat.completions.create(model=os.environ.get("OPENAI_MODEL", "google/gemini-2.5-flash"), messages=[{"role": "user", "content": prompt}], temperature=0.3)
    return response.choices[0].message.content

codons = ["ATG", "TAA", "GCT", "TGA", "TAG", "ATC", "TTT"]
print("Codon classification:")
for c in codons:
    if c == "ATG":
        category = "START"
    elif c in ("TAA", "TAG", "TGA"):
        category = "STOP"
    else:
        category = "coding"
    print(f"  {c}: {category}")

print("\n--- AI: Why does the genetic code work this way? ---\n")
result = ask_ai(
    "I classified DNA codons into START (ATG), STOP (TAA, TAG, TGA), and coding.\n\n"
    "Please explain:\n"
    "1. Why is ATG the universal start codon? Is it always the first codon?\n"
    "2. Why are there exactly three stop codons and not one?\n"
    "3. There are 64 possible codons (4^3) but only 20 amino acids. "
    "What does this 'redundancy' mean? Is it a bug or a feature?\n\n"
    "Use analogies a non-scientist would understand."
)
print(result)

#!/usr/bin/env python3


# A collection of short DNA sequences
sequences = [
    "ATGGCC",
    "GCTAGT",
    "TTACGA",
    "CCGATG",
    "AATTCC",
]

# Basic iteration
print("All sequences:")
for seq in sequences:
    print(f"  {seq}")

# With enumerate (numbered)
print("\nNumbered sequences:")
for i, seq in enumerate(sequences, start=1):
    print(f"  {i}. {seq} ({len(seq)} bases)")

# Lengths as a list
lengths = [len(s) for s in sequences]
print(f"\nLengths: {lengths}")
print(f"Total bases: {sum(lengths)}")
print(f"Average length: {sum(lengths) / len(lengths):.1f}")

# Filter: which sequences start with ATG?
print("\nSequences starting with ATG (potential start codons):")
starts_with_atg = [s for s in sequences if s.startswith("ATG")]
for seq in starts_with_atg:
    print(f"  {seq}")

# Parallel iteration with zip
labels = ["hemoglobin", "insulin", "p53", "BRCA1", "GAPDH"]
print("\nGene names and sequences:")
for label, seq in zip(labels, sequences):
    print(f"  {label}: {seq}")

#!/usr/bin/env python3


import os

try:
    from openai import OpenAI
    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", ""), base_url=os.environ.get("OPENAI_BASE_URL", "https://openrouter.ai/api/v1"))
    AI_AVAILABLE = True
except ImportError:
    AI_AVAILABLE = False
    print("Note: Install openai package for AI features\n")

def ask_ai(prompt: str) -> str:
    if not AI_AVAILABLE:
        return "(AI not available)"
    return client.chat.completions.create(model=os.environ.get("OPENAI_MODEL", "google/gemini-2.5-flash"), messages=[{"role": "user", "content": prompt}], temperature=0.3).choices[0].message.content

sequences = ["ATGGCC", "GCTAGT", "TTACGA", "CCGATG", "AATTCC"]

print("Our sequences:")
for i, seq in enumerate(sequences, 1):
    print(f"  {i}. {seq}")

print("\n--- AI: What are these sequences? ---\n")
result = ask_ai(
    f"I have {len(sequences)} short DNA sequences:\n" +
    "\n".join(f"  {i+1}. {s}" for i, s in enumerate(sequences)) +
    "\n\nFor each sequence:\n"
    "1. Translate it to protein (use the standard genetic code)\n"
    "2. Classify it: does it start with ATG? Does it contain a stop codon?\n"
    "3. If this were part of a real gene, what might it encode?\n\n"
    "Present as a table. Be brief."
)
print(result)