Upload 19 files

.gitignore

@@ -0,0 +1,9 @@
+venv/
+.env
+__pycache__/
+*.pyc
+.DS_Store
+
+node_modules/
+.vite/
+dist/

aiService.py

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+import requests
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def call_ai(prompt):
+
+    api_key = os.getenv("OPENROUTER_API_KEY")
+
+    response = requests.post(
+        url="https://openrouter.ai/api/v1/chat/completions",
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+        json={
+            "model": "openai/gpt-4o-mini-2024-07-18",
+            "messages": [
+                {"role": "user", "content": prompt}
+            ]
+        }
+    )
+
+    result = response.json()
+
+    if "choices" in result:
+        return result["choices"][0]["message"]["content"]
+
+    return "AI unavailable"

clusterService.py

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+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+
+
+def get_clusters(df):
+
+  df_processed = df.copy()
+
+  # --- Step 1: Drop ID columns ---
+  id_columns = [col for col in df_processed.columns if col.lower() == "id"]
+  df_processed = df_processed.drop(columns=id_columns)
+
+  # --- Step 2: Fill missing values ---
+  for col in df_processed.columns:
+    if pd.api.types.is_numeric_dtype(df_processed[col]):
+      df_processed[col] = df_processed[col].fillna(df_processed[col].mean())
+    else:
+      df_processed[col] = df_processed[col].fillna(df_processed[col].mode()[0])
+
+  # --- Step 3: Encode categorical columns (tiered strategy) ---
+  # One-Hot for low cardinality (safe, equal distances)
+  # Frequency for medium cardinality (no dimension explosion)
+  # Drop for high cardinality (likely ID-like, not useful)
+  encoded_columns = []
+  for col in df_processed.select_dtypes(include="object").columns:
+    n_unique = df_processed[col].nunique()
+
+    if n_unique <= 10:
+      # One-Hot: each category becomes a binary column
+      # All categories have equal distance (√2) from each other
+      dummies = pd.get_dummies(df_processed[col], prefix=col, dtype=int)
+      df_processed = pd.concat([df_processed.drop(columns=[col]), dummies], axis=1)
+      encoded_columns.append({"column": col, "method": "one-hot", "new_columns": n_unique})
+
+    elif n_unique <= 50:
+      # Frequency: replace category with how often it appears (proportion)
+      # No fake ordinal relationship, only 1 column
+      freq_map = df_processed[col].value_counts(normalize=True)
+      df_processed[col] = df_processed[col].map(freq_map).astype(float)
+      encoded_columns.append({"column": col, "method": "frequency", "unique_values": n_unique})
+
+    else:
+      # Drop: too many unique values (likely names, emails, IDs)
+      df_processed = df_processed.drop(columns=[col])
+      encoded_columns.append({"column": col, "method": "dropped", "reason": "too many unique values"})
+
+  # --- Step 4: Select numeric columns ---
+  df_numeric = df_processed.select_dtypes(include="number")
+
+  if df_numeric.shape[1] < 2:
+      return {
+          "status": "skipped",
+          "reason": "Not enough usable columns for clustering after encoding"
+      }
+
+  if df_numeric.var().mean() < 1e-3:
+      return {
+          "status": "skipped",
+          "reason": "Data has very low variance (no meaningful clusters)"
+      }
+  
+  # --- Step 5: Scale features ---
+  # StandardScaler: mean=0, std=1 for each feature
+  # Ensures all features contribute equally to distance calculations
+  scaler = StandardScaler()
+  scaled = scaler.fit_transform(df_numeric)
+
+  # --- Step 6: Find optimal k using Silhouette Score ---
+  # Silhouette measures how well each point fits in its cluster vs nearest other cluster
+  # Score ranges from -1 (wrong cluster) to +1 (perfect cluster)
+  # Pick the k with the highest average silhouette score
+  max_k = min(11, len(df_numeric))  # can't have more clusters than data points
+  if max_k < 3:
+      return {
+          "status": "skipped",
+          "reason": "Not enough data points for meaningful clustering"
+      }
+
+  silhouette_scores = []
+  for k in range(2, max_k):
+    km = KMeans(n_clusters=k, random_state=42, n_init=10)
+    labels = km.fit_predict(scaled)
+    score = silhouette_score(scaled, labels)
+    silhouette_scores.append(score)
+
+  best_k = silhouette_scores.index(max(silhouette_scores)) + 2
+
+  if max(silhouette_scores) < 0.15:
+      return {
+          "status": "skipped",
+          "reason": "No strong cluster separation found"
+      }
+
+  # --- Step 7: Final clustering with best k ---
+  km = KMeans(n_clusters=best_k, random_state=42, n_init=10)
+  km.fit(scaled)
+  df_numeric = df_numeric.copy()
+  df_numeric["cluster"] = km.labels_
+  cluster_summary = df_numeric.groupby("cluster").mean()
+  cluster_sizes = {
+    int(k): int(v)
+    for k, v in df_numeric["cluster"].value_counts().items()
+  }
+  
+  return {
+    "status": "success",
+    "best_k": best_k,
+    "silhouette_score": round(max(silhouette_scores), 4),
+    "encoded_columns": encoded_columns,
+    "cluster_summary": cluster_summary.to_dict(),
+    "cluster_sizes": cluster_sizes
+  }

clustering.py

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+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from backend.state import session_store
+from backend.services.clusterService import get_clusters
+
+router = APIRouter()
+
+class SessionRequest(BaseModel):
+  session_id: str
+
+@router.post("/clustering")
+async def clusters(request: SessionRequest):
+  session_id = request.session_id
+
+  if session_id not in session_store:
+    raise HTTPException(status_code=400, detail="session code not found")
+  
+  df = session_store[session_id]["df"]
+  
+  try:
+      return get_clusters(df)
+  except ValueError as e:
+      raise HTTPException(status_code=400, detail=str(e))

correlation.py

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+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from backend.state import session_store
+from backend.services.correlationService import get_correlation
+router = APIRouter()
+
+class SessionRequest(BaseModel):
+    session_id: str
+    target: str | None = None
+
+@router.post("/correlation")
+async def correlation(request: SessionRequest):
+    session_id = request.session_id
+    target = request.target
+
+    if session_id not in session_store:
+        raise HTTPException(status_code=400, detail="session code not found")
+
+    df = session_store[session_id]["df"]
+
+    try:
+        return get_correlation(df, target)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))

correlationService.py

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+import pandas as pd
+import math
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+from typing import Any, Dict, cast
+
+import math
+import numpy as np
+
+def clean_data(data):
+    ''' 
+      Make the API response JSON-safe.
+      FastAPI cannot return NaN/Inf in JSON.
+    '''
+    if isinstance(data, dict):
+        return {k: clean_data(v) for k, v in data.items()}
+
+    elif isinstance(data, list):
+        return [clean_data(v) for v in data]
+
+    elif isinstance(data, (np.integer, np.floating)):
+        value = float(data)
+        if math.isnan(value) or math.isinf(value):
+            return None
+        return value
+
+    elif isinstance(data, float):
+        if math.isnan(data) or math.isinf(data):
+            return None
+        return data
+
+    return data
+
+
+def get_correlation(df, target)-> Dict[str, Any]:
+
+  '''
+  Says this returns a dictionary
+  '''
+
+  df_processed = df.copy()
+
+  id_columns = [col for col in df_processed.columns if col.lower() == "id"]
+  df_processed = df_processed.drop(columns=id_columns)
+
+  for col in df_processed.columns:
+      if pd.api.types.is_numeric_dtype(df_processed[col]):
+          df_processed[col] = df_processed[col].fillna(df_processed[col].mean())
+      else:
+          df_processed[col] = df_processed[col].fillna(df_processed[col].mode()[0])
+
+  encoded_columns = []
+  le = LabelEncoder()
+  for col in df_processed.select_dtypes(include="object").columns:
+      df_processed[col] = le.fit_transform(df_processed[col])
+      encoded_columns.append(col)
+
+  # keep Numeric data
+  df_numeric = df_processed.select_dtypes(include="number")
+
+  # Remove constant columns
+  df_numeric = df_numeric.loc[:, df_numeric.nunique() > 1]
+
+  if df_numeric.shape[1] < 2:
+      return {
+          "message": "Not enough numeric columns for correlation"
+      }
+
+  # Always compute correlation
+  pearson_df = df_numeric.corr()
+  pearson_df = pearson_df.replace([np.inf, -np.inf], np.nan)
+  pearson_df = pearson_df.fillna(0)
+
+  pearson_corr = pearson_df.to_dict()
+  
+  spearman_df = df_numeric.corr(method="spearman")
+  spearman_df = spearman_df.replace([np.inf, -np.inf], np.nan)
+  spearman_df = spearman_df.fillna(0)
+
+  spearman_corr = spearman_df.to_dict()
+
+  # EDA
+
+  if not target:
+      '''
+      cast(type, value) = “pretend this value is this type”
+      '''
+      return cast(Dict[str, Any], clean_data({
+            "mode": "eda",
+            "rows": df.shape[0],
+            "columns": df.shape[1],
+            "column_names": df.columns.to_list(),
+            "encoded_columns": encoded_columns,
+            "final_column_count": df_numeric.shape[1],
+            "pearson": pearson_corr,
+            "spearman": spearman_corr
+        }))
+
+  # ML MODE 
+  
+  if target not in df_processed.columns:
+      raise ValueError(f"Target column '{target}' not found")
+
+  # Encode target if needed
+  if not pd.api.types.is_numeric_dtype(df_processed[target]):
+      df_processed[target] = LabelEncoder().fit_transform(df_processed[target])
+
+  X = df_processed.drop(columns=[target]).select_dtypes(include="number")
+  y = df_processed[target]
+
+  model = RandomForestClassifier(n_estimators=100, random_state=42)
+  model.fit(X, y)
+
+  feature_importance = {
+      col: round(float(imp), 4)
+      for col, imp in zip(X.columns, model.feature_importances_)
+  }
+
+  feature_importance = dict(
+      sorted(feature_importance.items(), key=lambda x: x[1], reverse=True)
+  )
+
+  return cast(Dict[str, Any], clean_data({
+        "mode": "ml",
+        "rows": df.shape[0],
+        "columns": df.shape[1],
+        "column_names": df.columns.to_list(),
+        "encoded_columns": encoded_columns,
+        "final_column_count": df_numeric.shape[1],
+        "pearson": pearson_corr,
+        "spearman": spearman_corr,
+        "feature_importance": dict(list(feature_importance.items())[:5]),
+    }))
+
+   

explain.py

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+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from backend.services.explainService import run_explain
+from backend.services.reportService import generate_report_file
+
+router = APIRouter()
+
+class SessionRequest(BaseModel):
+    session_id: str
+    target: str | None = None
+
+
+@router.post("/explain")
+async def explain(request: SessionRequest):
+    try:
+        return run_explain(request.session_id, request.target)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/report")
+async def report(request: SessionRequest):
+    try:
+        return generate_report_file(request.session_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

explainService.py

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+from backend.state import session_store
+from backend.services.profileService import run_profile
+from backend.services.correlationService import get_correlation
+from backend.services.clusterService import get_clusters
+from backend.services.aiService import call_ai
+from backend.services.promptService import build_prompt
+
+
+def run_explain(session_id, target):
+
+    if session_id not in session_store:
+        raise Exception("session not found")
+
+    df = session_store[session_id]["df"]
+
+    if target and target in df.columns:
+        mode = "ml"
+    else:
+        mode = "eda"
+        target = None
+
+    profile = run_profile(df)
+
+    if mode == "ml":
+        correlation = get_correlation(df, target)
+        top_features = correlation["feature_importance"]
+        pearson = correlation.get("pearson", {}) # Get pearson if it exists, otherwise use an empty dictionary instead of crashing
+        spearman = correlation.get("spearman", {}) #{} is just a safe backup value when "pearson" or "spearman" is missing.
+    else:
+        correlation = get_correlation(df, None)  
+        top_features = {}
+        pearson = correlation.get("pearson", {})
+        spearman = correlation.get("spearman", {})
+
+    try:
+        clusters = get_clusters(df)
+    except Exception:
+        clusters = {
+            "status": "skipped",
+            "reason": "Clustering failed"
+        }
+
+    summary = {
+        "rows": profile["rows"],
+        "columns": profile["columns"],
+        "numeric_columns": profile["numeric_column_count"],
+        "mode": mode,
+        "target": target,
+        "top_features": top_features,
+        "clusters": clusters,
+        "pearson": pearson,
+        "spearman": spearman
+    }
+
+    prompt = build_prompt(summary, profile)
+
+    ai_text = call_ai(prompt)
+
+    session_store[session_id]["summary"] = summary
+    session_store[session_id]["explanation"] = ai_text
+
+    return {
+        "summary": summary,
+        "explanation": ai_text
+    }

main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from dotenv import load_dotenv
+import os
+from backend.routers import upload, profile, correlation, clustering, explain
+
+load_dotenv()
+
+app = FastAPI(title="Explain My Data API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/health")
+def health_check():
+    return {"status": "ok", "message": "Server is running"}
+
+app.include_router(upload.router, prefix="/api") 
+app.include_router(profile.router, prefix="/api")
+app.include_router(correlation.router, prefix="/api")
+app.include_router(clustering.router, prefix="/api")
+app.include_router(explain.router, prefix="/api")

package-lock.json

@@ -0,0 +1,1293 @@
+{
+  "name": "DataDecoder",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "dependencies": {
+        "@tailwindcss/typography": "^0.5.19",
+        "react-markdown": "^10.1.0"
+      }
+    },
+    "node_modules/@tailwindcss/typography": {
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+      "resolved": "https://registry.npmjs.org/@tailwindcss/typography/-/typography-0.5.19.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "postcss-selector-parser": "6.0.10"
+      },
+      "peerDependencies": {
+        "tailwindcss": ">=3.0.0 || insiders || >=4.0.0-alpha.20 || >=4.0.0-beta.1"
+      }
+    },
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+    },
+    "node_modules/zwitch": {
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+      "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz",
+      "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==",
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+}

package.json

@@ -0,0 +1,6 @@
+{
+  "dependencies": {
+    "@tailwindcss/typography": "^0.5.19",
+    "react-markdown": "^10.1.0"
+  }
+}

plotService.py

@@ -0,0 +1,45 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+
+def create_correlation_heatmap(corr_data, session_id):
+    plt.figure(figsize=(6, 4))
+
+    sns.heatmap(corr_data, annot=False, cmap="coolwarm")
+
+    file_path = f"{session_id}_correlation.png"
+    plt.title("Correlation Heatmap")
+    plt.savefig(file_path, bbox_inches="tight")
+    plt.close()
+
+    return file_path
+
+def create_feature_importance_chart(feature_data, session_id):
+    plt.figure(figsize=(6, 4))
+
+    names = list(feature_data.keys())
+    values = list(feature_data.values())
+
+    plt.barh(names, values)
+    plt.title("Top Features")
+
+    file_path = f"{session_id}_features.png"
+    plt.savefig(file_path, bbox_inches="tight")
+    plt.close()
+
+    return file_path
+
+def create_cluster_chart(cluster_sizes, session_id):
+    plt.figure(figsize=(6, 4))
+
+    labels = list(cluster_sizes.keys())
+    values = list(cluster_sizes.values())
+
+    plt.bar(labels, values)
+    plt.title("Cluster Distribution")
+
+    file_path = f"{session_id}_clusters.png"
+    plt.savefig(file_path, bbox_inches="tight")
+    plt.close()
+
+    return file_path

profile.py

@@ -0,0 +1,19 @@
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from backend.state import session_store
+from backend.services.profileService import run_profile
+
+router = APIRouter()
+
+class SessionRequest(BaseModel):
+    session_id: str
+
+@router.post("/profile")
+async def profile(request: SessionRequest):
+    session_id = request.session_id
+
+    if session_id not in session_store:
+        raise HTTPException(status_code=400, detail="session code not found")
+
+    df = session_store[session_id]["df"]
+    return run_profile(df)

profileService.py

@@ -0,0 +1,37 @@
+import pandas as pd
+
+def run_profile(df):
+    rows, cols = df.shape
+    rows = int(rows)
+    cols = int(cols)
+    columns = df.columns.to_list()
+    column_details = []
+    numeric_count = 0
+
+    for col in columns:
+        null_count = int(df[col].isnull().sum())
+        null_percentage = round((null_count / rows) * 100, 2)
+
+        column = {
+            col: str(df[col].dtype),
+            "null_count": null_count,
+            "null_percentage": null_percentage,
+            "high_null_warning": null_percentage > 50
+        }
+
+        if pd.api.types.is_numeric_dtype(df[col]):
+            numeric_count += 1
+            column["mean"] = round(float(df[col].mean()), 2)
+            column["median"] = round(float(df[col].median()), 2)
+            column["min"] = round(float(df[col].min()), 2)
+            column["max"] = round(float(df[col].max()), 2)
+
+        column_details.append(column)
+
+    return {
+        "rows": rows,
+        "columns": cols,
+        "column_name": df.columns.to_list(),
+        "column_details": column_details,
+        "numeric_column_count": numeric_count
+    }

promptService.py

@@ -0,0 +1,111 @@
+def build_prompt(summary, profile):
+
+    column_names = ", ".join(profile["column_name"])
+
+    column_details = "\n".join([
+        f"- {list(col.keys())[0]} | type: {list(col.values())[0]}"
+        for col in profile["column_details"]
+    ])
+
+    features_text = "\n".join(
+        [f"- {k}: {v}" for k, v in summary["top_features"].items()]
+    )
+
+    pearson_text = "\n".join([
+        f"- {a} & {b}: {round(v, 2)}"
+        for a, b, v in summary.get("top_pearson", [])
+    ])
+
+    spearman_text = "\n".join([
+        f"- {a} & {b}: {round(v, 2)}"
+        for a, b, v in summary.get("top_spearman", [])
+    ])
+
+    clusters = summary["clusters"]
+
+    if clusters.get("status") == "success":
+        cluster_summary = clusters.get("cluster_summary", {})
+
+        cluster_summary_text = "\n".join([
+            f"Cluster {k}: {v}" for k, v in cluster_summary.items()
+        ])
+
+        cluster_text = f"""
+Cluster Analysis:
+Best k: {clusters.get('best_k')}
+
+Cluster Sizes:
+{clusters.get('cluster_sizes')}
+
+Cluster Summary:
+{cluster_summary_text}
+"""
+    else:
+        cluster_text = f"""
+Cluster Analysis:
+Not applied
+
+Reason:
+{clusters.get("reason")}
+"""
+
+    prompt = f"""
+You are a professional data analyst.
+
+Generate a structured data analysis report.
+
+Format:
+1. Dataset Overview
+2. Key Features
+3. Cluster Analysis
+4. Key Insights
+5. Conclusion
+
+Dataset:
+- Rows: {summary['rows']}
+- Columns: {summary['columns']}
+- Numeric columns: {summary['numeric_columns']}
+
+Column Names:
+{column_names}
+
+Column Details:
+{column_details}
+"""
+    
+    prompt += f"""
+Correlation Analysis:
+
+Top Pearson (linear relationships):
+{pearson_text}
+
+Top Spearman (rank relationships):
+{spearman_text}
+"""
+
+    if summary["mode"] == "ml":
+        prompt += f"""
+Target Column:
+{summary['target']}
+
+Top Features:
+{features_text}
+"""
+    else:
+        prompt += """
+No target column.
+
+Focus on patterns and relationships.
+"""
+
+    prompt += cluster_text
+
+    prompt += """
+Instructions:
+- Use simple English
+- No conversational text
+- Use bullet points
+- Use real column names
+"""
+
+    return prompt

reportService.py

@@ -0,0 +1,85 @@
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.enums import TA_CENTER
+from fastapi.responses import FileResponse
+from backend.state import session_store
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+
+def generate_pdf(text, file_path):
+
+    from reportlab.lib.enums import TA_CENTER
+
+    doc = SimpleDocTemplate(file_path)
+    styles = getSampleStyleSheet()
+
+    title_style = ParagraphStyle(
+        name="Title",
+        parent=styles["Heading1"],
+        alignment=TA_CENTER,
+        spaceAfter=20
+    )
+
+    section_style = ParagraphStyle(
+        name="Section",
+        parent=styles["Heading2"],
+        spaceBefore=15,
+        spaceAfter=10
+    )
+
+    bullet_style = ParagraphStyle(
+        name="Bullet",
+        parent=styles["Normal"],
+        leftIndent=15,
+        spaceAfter=5
+    )
+
+    normal_style = styles["Normal"]
+
+    content = []
+
+    lines = text.split("\n")
+
+    for line in lines:
+        line = line.strip()
+
+        line = line.replace("**", "")
+
+        if not line:
+            content.append(Spacer(1, 10))
+            continue
+
+        if "data analysis report" in line.lower():
+            content.append(Paragraph(line, title_style))
+
+        elif line[0].isdigit() and "." in line:
+            content.append(Paragraph(line, section_style))
+
+        elif line.startswith("*") or line.startswith("-"):
+            bullet = line.lstrip("*- ").strip()
+            content.append(Paragraph(f"• {bullet}", bullet_style))
+
+        else:
+            content.append(Paragraph(line, normal_style))
+
+    doc.build(content)
+
+def generate_report_file(session_id):
+
+    if session_id not in session_store:
+        raise Exception("session not found")
+
+    ai_text = session_store[session_id].get("explanation")
+
+    if not ai_text:
+        raise Exception("Run /explain first")
+
+    file_path = f"report_{session_id}.pdf"
+
+    generate_pdf(ai_text, file_path)
+
+    return FileResponse(
+        path=file_path,
+        filename="report.pdf",
+        media_type="application/pdf"
+    )

requirements.txt

@@ -0,0 +1,10 @@
+fastapi
+uvicorn[standard]
+python-dotenv
+pandas
+numpy
+scikit-learn
+scipy
+python-multipart
+requests
+reportlab

state.py

@@ -0,0 +1 @@
+session_store = {}

upload.py

@@ -0,0 +1,53 @@
+from fastapi import APIRouter, UploadFile, File, HTTPException
+import io
+import pandas as pd
+from backend.state import session_store
+import uuid
+import math
+
+router = APIRouter()
+
+@router.post("/upload")
+async def upload_file(file: UploadFile=File(...)):
+  session_id = str(uuid.uuid4())
+  if not file.filename.endswith(".csv"): # type: ignore
+    raise HTTPException(status_code=400, detail="Only CSV files are accepted")
+  
+  content = await file.read()
+
+  if len(content) > 20 * 1024 * 1024:
+        raise HTTPException(status_code=400, detail="File too large. Maximum size is 10MB")
+  
+  try:
+      df = pd.read_csv(io.BytesIO(content), na_values=['?', 'NA', 'N/A', 'na', 'n/a', ''])
+  except Exception:
+      raise HTTPException(status_code=400, detail="Could not parse CSV. Check the file format")
+  
+  def clean_nan(data):
+    if isinstance(data, dict):
+        return {k: clean_nan(v) for k, v in data.items()}
+    elif isinstance(data, list):
+        return [clean_nan(v) for v in data]
+    elif isinstance(data, float) and math.isnan(data):
+        return None
+    return data
+
+  rows, cols = df.shape
+  preview = clean_nan(df.head(5).to_dict(orient="records"))
+  session_store[session_id] = {
+      "df": df,
+      "summary": None,
+      "explanation": None
+  }
+
+  return {
+      "filename": file.filename,
+      "rows": rows,
+      "columns": cols,
+      "column_names": df.columns.tolist(),
+      "session_id": session_id,
+      "preview": preview
+  }
+
+
+