diff --git a/week6/community-contributions/week_6_exercise_revised.py b/week6/community-contributions/week_6_exercise_revised.py
new file mode 100644
index 0000000..bcf9ae1
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+++ b/week6/community-contributions/week_6_exercise_revised.py
@@ -0,0 +1,621 @@
+# -*- coding: utf-8 -*-
+"""Week_6_exercise_revised.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1GaV053HB8l-Wd3J3o9BcOAjC009Qk_W0
+"""
+
+#installations
+!pip install --upgrade pip
+!pip install datasets==3.0.1 anthropic transformers accelerate pandas tqdm numpy
+
+#imports
+import os
+import re
+import json
+import random
+import time
+from typing import Optional, List, Dict, Any, Tuple
+from sklearn.model_selection import train_test_split
+import anthropic
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import seaborn as sns
+
+#TEMPORARY: Hard-coded keys
+
+#I hid my keys, you can replace your keys with 'sk' and 'hf'
+os.environ["ANTHROPIC_API_KEY"] = "sk"
+os.environ["HF_TOKEN"] = "hf"
+
+
+# Anthropic Client
+try:
+    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
+    print("Anthropic client initialized")
+except Exception as e:
+    raise ImportError("Please install anthropic: !pip install anthropic") from e
+
+#some Basic configrations used throughtout the notebook
+RANDOM_SEED = 42
+# medium test size
+TEST_SIZE = 50
+CLAUDE_MODEL = "claude-opus-4-20250514"
+MAX_TOKENS = 300
+
+random.seed(RANDOM_SEED)
+np.random.seed(RANDOM_SEED)
+
+# Load my dataset, the Aplliances in my case
+dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", "raw_meta_Appliances", split="full")
+#using Pandas to create a dataframe
+df = dataset.to_pandas()
+#see the data
+df.head()
+
+# Let clean the Price column and have it as a Price-clean
+df["price_clean"] = pd.to_numeric(df["price"], errors="coerce")
+
+#check the number of rows In the ddata
+print("Dataset size:", len(df))
+
+#check The featues in the data
+print(df.columns.tolist())
+
+#checking some info
+print(df.info())
+
+print("Price-related columns found:", [c for c in df.columns if "price" in c])
+
+print("Missing price_clean:", df["price_clean"].isna().sum(), "rows")
+
+# Price distribution visualization (Zoomed histogram)
+plt.figure(figsize=(10,5))
+df[df["price_clean"] < 200]["price_clean"].hist(bins=50)
+plt.title("Price Distribution")
+plt.xlabel("Price ($)")
+plt.ylabel("Frequency")
+plt.show()
+
+# Keep only rows where price_clean is not null
+df_model = df.dropna(subset=["price_clean"]).copy()
+
+# come up with a ptompt text combined
+def combine_text(row):
+    title = row["title"] or ""
+    features = " ".join(row["features"]) if isinstance(row["features"], list) else ""
+    description = " ".join(row["description"]) if isinstance(row["description"], list) else ""
+    return f"{title}\n\nFEATURES: {features}\n\nDESCRIPTION: {description}"
+
+df_model["text"] = df_model.apply(combine_text, axis=1)
+
+# Retain what's needed
+df_model = df_model[["text", "price_clean"]].reset_index(drop=True)
+
+# check the model dataset size
+print(len(df_model))
+df_model.head(5)
+
+# Splitting the data into Training and test
+train_df, test_df = train_test_split(
+    df_model,
+    test_size=0.10,  # 10% test split
+    random_state=RANDOM_SEED
+)
+
+#Training
+len(train_df)
+
+#Testing
+len(test_df)
+
+# make the test a list for better samplng
+test_records = test_df.to_dict(orient="records")
+
+# Pricing system Prompt
+
+def build_prompt(item_text: str) -> str:
+    return f"""
+You are a pricing analyst. Given a marketplace product listing, estimate the item's correct fair market price in KES.
+
+Return ONLY a number, no currency sign, no explanation.
+
+Product details:
+\"\"\"
+{item_text}
+\"\"\"
+"""
+
+def estimate_price_claude(item_text: str) -> Optional[float]:
+    try:
+        prompt = build_prompt(item_text)
+
+        response = client.messages.create(
+            model=CLAUDE_MODEL,
+            max_tokens=MAX_TOKENS,
+            messages=[
+                {"role": "user", "content": prompt}
+            ]
+        )
+
+        raw_output = response.content[0].text.strip()
+
+        # Extract first valid number from model response
+        match = re.search(r"\d+(\.\d+)?", raw_output.replace(",", ""))
+        return float(match.group(0)) if match else None
+
+    except Exception as e:
+        print("Error:", e)
+        return None
+
+client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
+
+# Filter and Sample 100 usable Rows
+df_usable = df[df["price_clean"].notna()].copy()
+sample_df = df_usable.sample(100, random_state=42).reset_index(drop=True)
+
+#empty predriction list for them to be stored
+predictions = []
+
+#Getting the prices
+def extract_price(text):
+    """Extract the first valid float from Claude's reply."""
+    match = re.search(r"\d+(\.\d+)?", text.replace(",", ""))
+    return float(match.group(0)) if match else None
+
+# Getting the predictions
+for i, row in tqdm(sample_df.iterrows(), total=len(sample_df)):
+    title = row["title"]
+    desc = " ".join(row["description"]) if isinstance(row["description"], list) else str(row["description"])
+    feat = " ".join(row["features"]) if isinstance(row["features"], list) else str(row["features"])
+    cats = " ".join(row["categories"]) if isinstance(row["categories"], list) else str(row["categories"])
+
+    prompt = f"""
+You are estimating the USD retail price of an appliance part.
+
+Analyze the information and respond with **only a single number** (no currency symbol, no text, no explanation).
+
+TITLE: {title}
+DESCRIPTION: {desc}
+FEATURES: {feat}
+CATEGORIES: {cats}
+
+Your response must be only a number like: 29.99
+"""
+
+    response = client.messages.create(
+        model=CLAUDE_MODEL,
+        max_tokens=50,
+        messages=[{"role": "user", "content": prompt}]
+    )
+
+    raw = response.content[0].text.strip()
+    pred_price = extract_price(raw)
+
+    predictions.append({
+        "title": title,
+        "true_price": row["price_clean"],
+        "claude_price": pred_price,
+        "raw_reply": raw
+    })
+
+# Saving output in a csv nw
+result_df = pd.DataFrame(predictions)
+result_df.to_csv("claude_price_predictions_100.csv", index=False)
+
+# Show preview
+display(result_df.head())
+
+# Error metrics
+valid = result_df[result_df["claude_price"].notna()]
+mae = np.mean(np.abs(valid["true_price"] - valid["claude_price"]))
+rmse = np.sqrt(np.mean((valid["true_price"] - valid["claude_price"])**2))
+pct_within_20 = np.mean(np.abs(valid["true_price"] - valid["claude_price"]) <= 20) * 100
+
+print(f"\nValid predictions: {len(valid)}/{len(result_df)}")
+print(f"MAE: {mae:.2f}")
+print(f"RMSE: {rmse:.2f}")
+print(f"% within $20: {pct_within_20:.1f}%")
+
+"""The model returned a price every single time:
+
+
+
+1.   -->MAE = 22.52	On average Claude is off by 22.52 from the true price
+2.   -->RMSE = 44.11	Big errors exist on some items — a sign of occasional wild guesses
+2.   -->RMSE = 44.11	Big errors exist on some items — a sign of occasional wild guesses
+2.   -->72% within $20	Claude predicts reasonable accuracy on most products, but 28% are far off.
+
+;
+
+1.   Strengths- Model is somehow decent with zero/low fine-tuning. It understood the task, 72% within $20 on a dataset it’s never seen is a good baseline
+1.   Weaknesses- Too many rounded “classic” retail numbers (24.99, 89.99, 14.99, 29.99). Seems not to deeply use features, category, or rating. Also the RMSE is high → meaning a few really bad errors are dragging performance
+
+Improvements
+
+1. Prompt enhancements
+2. Multi-shot and also better structuring
+3. Fine-tuning with local model
+"""
+
+#Now we build a persona Prompt
+def build_pricing_prompt(examples: list, new_title: str) -> str:
+    """
+    Build a multi-shot prompt for the E-commerce Market Analyst persona.
+    Each example has (title, price).
+    """
+    few_shots = "\n".join(
+        [f"Product: {t}\nEstimated fair market price: ${p:.2f}" for t, p in examples]
+    )
+
+    system_prompt = (
+        "You are a meticulous Data-Driven Market Analyst who estimates realistic, data-based "
+        "product prices for online marketplaces. You base estimates on comparable items and "
+        "avoid outliers. Return only the price number."
+    )
+
+    user_prompt = (
+        f"{system_prompt}\n\nHere are recent examples:\n{few_shots}\n\n"
+        f"Now estimate a fair market price for this product:\n"
+        f"Product: {new_title}\n\n"
+        "Respond with only a number, no text or symbols."
+    )
+    return user_prompt
+
+#10-shot predictios
+subset_10 = df.dropna(subset=["price_clean"]).sample(10, random_state=42).reset_index(drop=True)
+few_shots_3 = subset_10.sample(3, random_state=42)[["title", "price_clean"]].values.tolist()
+results_10 = []
+
+for i, row in tqdm(subset_10.iterrows(), total=len(subset_10)):
+    prompt = build_pricing_prompt(few_shots_3, row["title"])
+    try:
+        resp = client.messages.create(
+            model=CLAUDE_MODEL,
+            max_tokens=MAX_TOKENS,
+            messages=[{"role": "user", "content": prompt}],
+        )
+        reply = resp.content[0].text.strip()
+        pred = float(reply.replace("$", "").strip())
+    except Exception:
+        pred, reply = np.nan, None
+    results_10.append({"title": row["title"], "true_price": row["price_clean"], "pred_price": pred, "raw": reply})
+
+df10 = pd.DataFrame(results_10).dropna(subset=["pred_price"])
+
+mae10 = np.mean(np.abs(df10.pred_price - df10.true_price))
+
+rmse10 = np.sqrt(np.mean((df10.pred_price - df10.true_price)**2))
+
+pct20_10 = np.mean(np.abs(df10.pred_price - df10.true_price) <= 20) * 100
+
+print(f"MAE={mae10:.2f}, RMSE={rmse10:.2f}, %within$20={pct20_10:.1f}%")
+df10.head()
+
+#30 shot
+subset_30 = df.dropna(subset=["price_clean"]).sample(30, random_state=42).reset_index(drop=True)
+few_shots_5 = subset_30.sample(5, random_state=42)[["title", "price_clean"]].values.tolist()
+results_30 = []
+
+for i, row in tqdm(subset_30.iterrows(), total=len(subset_30)):
+    prompt = build_pricing_prompt(few_shots_5, row["title"])
+    try:
+        resp = client.messages.create(
+            model=CLAUDE_MODEL,
+            max_tokens=MAX_TOKENS,
+            messages=[{"role": "user", "content": prompt}],
+        )
+        reply = resp.content[0].text.strip()
+        pred = float(reply.replace("$", "").strip())
+    except Exception:
+        pred, reply = np.nan, None
+    results_30.append({"title": row["title"], "true_price": row["price_clean"], "pred_price": pred, "raw": reply})
+
+df30 = pd.DataFrame(results_30).dropna(subset=["pred_price"])
+
+mae30 = np.mean(np.abs(df30.pred_price - df30.true_price))
+
+rmse30 = np.sqrt(np.mean((df30.pred_price - df30.true_price)**2))
+
+pct20_30 = np.mean(np.abs(df30.pred_price - df30.true_price) <= 20) * 100
+
+print(f"MAE={mae30:.2f}, RMSE={rmse30:.2f}, %within$20={pct20_30:.1f}%")
+df30.head()
+
+#50 Shot s
+subset_50 = df.dropna(subset=["price_clean"]).sample(50, random_state=42).reset_index(drop=True)
+few_shots_8 = subset_50.sample(8, random_state=42)[["title", "price_clean"]].values.tolist()
+results_50 = []
+
+for i, row in tqdm(subset_50.iterrows(), total=len(subset_50)):
+    prompt = build_pricing_prompt(few_shots_8, row["title"])
+    try:
+        resp = client.messages.create(
+            model=CLAUDE_MODEL,
+            max_tokens=MAX_TOKENS,
+            messages=[{"role": "user", "content": prompt}],
+        )
+        reply = resp.content[0].text.strip()
+        pred = float(reply.replace("$", "").strip())
+    except Exception:
+        pred, reply = np.nan, None
+    results_50.append({"title": row["title"], "true_price": row["price_clean"], "pred_price": pred, "raw": reply})
+
+df50 = pd.DataFrame(results_50).dropna(subset=["pred_price"])
+
+mae50 = np.mean(np.abs(df50.pred_price - df50.true_price))
+
+rmse50 = np.sqrt(np.mean((df50.pred_price - df50.true_price)**2))
+
+pct20_50 = np.mean(np.abs(df50.pred_price - df50.true_price) <= 20) * 100
+
+print(f"MAE={mae50:.2f}, RMSE={rmse50:.2f}, %within$20={pct20_50:.1f}%")
+df50.head()
+
+#Improved Ptompt and comparin the 10,30, &50 shot hints
+def build_strict_prompt(few_shots, test_title):
+    shots_text = "\n".join([f"Title: {t}\nPrice: ${p:.2f}" for t, p in few_shots])
+    return f"""
+You are an expert e-commerce product pricing analyst. Your job is to predict the most realistic market price for a product based purely on its title.
+
+Here are reference examples:
+{shots_text}
+
+Now predict the price for:
+Title: {test_title}
+
+RULES:
+- Return ONLY a single number.
+- No dollar sign.
+- No text, no reasoning, no words.
+- Format: 123.45
+"""
+
+def run_eval(name, subset, shot_count):
+    few = subset.sample(shot_count, random_state=42)[["title", "price_clean"]].values.tolist()
+    results = []
+
+    for _, row in tqdm(subset.iterrows(), total=len(subset), desc=f"{name}"):
+        prompt = build_strict_prompt(few, row["title"])
+        try:
+            resp = client.messages.create(
+                model=CLAUDE_MODEL,
+                max_tokens=MAX_TOKENS,
+                messages=[{"role": "user", "content": prompt}],
+            )
+            reply = resp.content[0].text.strip()
+            pred = float(reply)
+        except Exception:
+            pred, reply = np.nan, None
+
+        results.append({"title": row["title"], "true": row["price_clean"], "pred": pred})
+
+    df = pd.DataFrame(results).dropna(subset=["pred"])
+    mae = np.mean(np.abs(df.pred - df.true))
+    rmse = np.sqrt(np.mean((df.pred - df.true)**2))
+    pct20 = np.mean(np.abs(df.pred - df.true) <= 20) * 100
+    return df, mae, rmse, pct20
+
+# Run 10 / 30 / 50
+subset10 = df.dropna(subset=["price_clean"]).sample(10, random_state=1).reset_index(drop=True)
+subset30 = df.dropna(subset=["price_clean"]).sample(30, random_state=2).reset_index(drop=True)
+subset50 = df.dropna(subset=["price_clean"]).sample(50, random_state=3).reset_index(drop=True)
+
+df10, mae10, rmse10, pct10 = run_eval("RUN10", subset10, 3)
+df30, mae30, rmse30, pct30 = run_eval("RUN30", subset30, 6)
+df50, mae50, rmse50, pct50 = run_eval("RUN50", subset50, 8)
+
+#compare
+comparison = pd.DataFrame([
+    {"shots": 10, "MAE": mae10, "RMSE": rmse10, "%≤$20": pct10},
+    {"shots": 30, "MAE": mae30, "RMSE": rmse30, "%≤$20": pct30},
+    {"shots": 50, "MAE": mae50, "RMSE": rmse50, "%≤$20": pct50},
+])
+
+print(comparison)
+comparison
+
+"""The model becomes confused by too many examples, became more biased toward random values and less less stable and less accurate.
+Hypothesis: Possibly the dataset has high variance (many unrelated categories), and the model benefits from small, clean, representative few-shots, not large few-shots.
+"""
+
+#Rechecking the variance in the data
+prices = df["price_clean"].dropna()
+print(prices.describe(percentiles=[0.25, 0.5, 0.75, 0.9, 0.95]))
+
+print("\nSkewness:", prices.skew())
+print("Kurtosis:", prices.kurt())
+
+# Plot histogram
+plt.figure(figsize=(12,4))
+sns.histplot(prices, bins=50)
+plt.title("Histogram — Full Dataset Price Distribution")
+plt.xlabel("Price ($)")
+plt.ylabel("Frequency")
+plt.show()
+
+# Plot boxplot
+plt.figure(figsize=(10,2))
+sns.boxplot(x=prices)
+plt.title("Boxplot — Full Dataset Price Spread")
+plt.show()
+
+"""Testing fewer shots to check fr the optimal"""
+
+def run_few_shot_test(df_subset, shots, model=CLAUDE_MODEL):
+    few_shots = df_subset.sample(shots, random_state=42)[["title", "price_clean"]].values.tolist()
+    results = []
+
+    for _, row in df_subset.iterrows():
+        prompt = build_pricing_prompt(few_shots, row["title"])
+        try:
+            resp = client.messages.create(
+                model=model,
+                max_tokens=MAX_TOKENS,
+                messages=[{"role": "user", "content": prompt}],
+            )
+            reply = resp.content[0].text.strip()
+            pred = float(reply.replace("$", "").strip())
+        except:
+            pred, reply = np.nan, None
+
+        results.append({"title": row["title"], "true": row["price_clean"], "pred": pred})
+
+    df_res = pd.DataFrame(results).dropna()
+    mae = np.mean(np.abs(df_res.pred - df_res.true))
+    rmse = np.sqrt(np.mean((df_res.pred - df_res.true)**2))
+    pct20 = np.mean(np.abs(df_res.pred - df_res.true) <= 20) * 100
+    return df_res, mae, rmse, pct20
+
+#Tabulate the 2 shot results
+df2, mae2, rmse2, pct2 = run_few_shot_test(subset_50, shots=2)
+print("2-SHOT RESULTS → MAE={:.2f}, RMSE={:.2f}, %≤$20={:.1f}%".format(mae2, rmse2, pct2))
+df2.head()
+
+#5 shot results
+df5, mae5, rmse5, pct5 = run_few_shot_test(subset_50, shots=5)
+print("5-SHOT RESULTS → MAE={:.2f}, RMSE={:.2f}, %≤$20={:.1f}%".format(mae5, rmse5, pct5))
+df5.head()
+
+#7 shot results
+df7, mae7, rmse7, pct7 = run_few_shot_test(subset_50, shots=7)
+print("7-SHOT RESULTS → MAE={:.2f}, RMSE={:.2f}, %≤$20={:.1f}%".format(mae7, rmse7, pct7))
+df7.head()
+
+#Tabulate all the shots to choose the optimal or if there is Any need for the shots
+
+results_summary = [
+    {"shots": 0,  "MAE": 22.52, "RMSE": 44.11,  "%≤$20": 72.0},   # baseline
+    {"shots": 2,  "MAE": mae2, "RMSE": rmse2, "%≤$20": pct2},
+    {"shots": 5,  "MAE": mae5, "RMSE": rmse5, "%≤$20": pct5},
+    {"shots": 7,  "MAE": mae7, "RMSE": rmse7, "%≤$20": pct7},
+    {"shots": 10, "MAE": 16.27, "RMSE": 38.59, "%≤$20": 90.0},
+    {"shots": 30, "MAE": 135.73, "RMSE": 606.78, "%≤$20": 70.0},
+    {"shots": 50, "MAE": 42.54, "RMSE": 136.61, "%≤$20": 72.0},
+]
+
+df_comparison = pd.DataFrame(results_summary)
+df_comparison = df_comparison.sort_values("shots").reset_index(drop=True)
+df_comparison
+
+"""1. 0-shot baseline: MAE 22.52, %≤$20 72%
+
+2. Very low few-shots (2, 5): Surprisingly worse than baseline (MAE ↑, %≤$20 ↓), likely due to variance and poor example selection.
+
+3. 7-shot: Improves over baseline slightly, MAE 19.91, %≤$20 back to 72%
+
+4. 10-shot: Best performance overall — MAE 16.27, %≤$20 jumps to 90%! Clearly the few-shot hints are helping here.
+
+5. 30-shot: Performance collapses (MAE 135.73, RMSE 606.78) — too many examples may confuse the model.
+
+6. 50-shot: Slightly better than 30-shot but still worse than 10-shot.
+
+
+Conclusion: Optimal few-shot count is 10 for this dataset and prompt style.
+"""
+
+#Further finetuning of the selected 10-shot
+
+def build_finetune_prompt(few_shots: list, target_title: str, max_chars=800):
+    """
+    few_shots: list of dicts {"title":..., "price_clean":...}
+    target_title: title string
+    """
+    parts = ["You are an e-commerce pricing expert. Estimate product prices in USD accurately. Output only a number."]
+    parts.append("\nExamples:")
+    for ex in few_shots:
+        parts.append(f"- {ex['title']}: {ex['price_clean']}")
+    parts.append("\nPredict price for the following product:")
+    parts.append(f"Title: {target_title}")
+    prompt = "\n".join(parts)
+    if len(prompt) > max_chars:
+        return prompt[:max_chars] + "..."
+    return prompt
+
+# Sample 10-shot prompts for fine-tuning
+finetune_examples = []
+subset_10 = df.dropna(subset=["price_clean"]).sample(100, random_state=42).reset_index(drop=True)  # 100 products for initial fine-tuning
+
+for idx, row in subset_10.iterrows():
+    # Pick 10 random examples from subset for few-shot
+    few_shots = subset_10.drop(idx).sample(10, random_state=idx)[["title","price_clean"]].to_dict(orient="records")
+    prompt = build_finetune_prompt(few_shots, row["title"])
+    finetune_examples.append({
+        "prompt": prompt,
+        "completion": str(row["price_clean"])
+    })
+
+print("Sample fine-tuning example:")
+print(finetune_examples[0])
+
+with open("finetune_10shot.jsonl", "w") as f:
+    for ex in finetune_examples:
+        f.write(json.dumps(ex) + "\n")
+print("(10-shot format).finetuned")
+
+# Evaluate enhanced 10-shot prompt on sample
+results_finetune_test = []
+
+for idx, row in subset_10.iterrows():
+    few_shots = subset_10.drop(idx).sample(10, random_state=idx)[["title","price_clean"]].to_dict(orient="records")
+    prompt = build_finetune_prompt(few_shots, row["title"])
+    try:
+        resp = client.messages.create(
+            model=CLAUDE_MODEL,
+            max_tokens=MAX_TOKENS,
+            messages=[{"role": "user", "content": prompt}]
+        )
+        reply = resp.content[0].text.strip()
+        pred = float(reply.replace("$","").strip())
+    except Exception:
+        pred, reply = np.nan, None
+    results_finetune_test.append({"title": row["title"], "true_price": row["price_clean"], "pred": pred, "raw": reply})
+
+df_finetune_test = pd.DataFrame(results_finetune_test).dropna(subset=["pred"])
+mae_ft = np.mean(np.abs(df_finetune_test.pred - df_finetune_test.true_price))
+rmse_ft = np.sqrt(np.mean((df_finetune_test.pred - df_finetune_test.true_price)**2))
+pct20_ft = np.mean(np.abs(df_finetune_test.pred - df_finetune_test.true_price) <= 20) * 100
+
+print(f"Finetuned 10-shot performance: MAE={mae_ft:.2f}, RMSE={rmse_ft:.2f}, %≤$20={pct20_ft:.1f}%")
+
+"""Multi-shot prompting (10 examples in the prompt) without fine-tuning performed much better.
+
+
+Next trial: Prompt optimization
+"""
+
+#prompt optimization seems like th eonly choice
+def build_pricing_prompt_alt(few_shots: list, target_title: str) -> str:
+    """
+    Build an alternative multi-shot pricing prompt for Claude.
+
+    few_shots: list of dicts with keys 'title' and 'price_clean'
+    target_title: product title to predict the price for
+    """
+    parts = []
+
+    # Instruction with a slightly different phrasing
+    parts.append("Act as an expert e-commerce pricing analyst.")
+    parts.append("Given product titles and their prices, predict the price in USD for the new product.")
+    parts.append("Only provide the numeric price. No extra text, explanations, or symbols.")
+
+    # Format the examples differently: numbered list
+    parts.append("\nExample prices:")
+    for i, ex in enumerate(few_shots, start=1):
+        parts.append(f"{i}. {ex['title']} — ${ex['price_clean']:.2f}")
+
+    # Target product
+    parts.append("\nPredict the price for this product:")
+    parts.append(f"Title: {target_title}")
+    parts.append("Price (USD):")
+
+    # Combine into single prompt
+    prompt = "\n".join(parts)
+    return prompt
+
+"""eda"""
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