app/logic/calc_a.py

import pandas as pd
import google.generativeai as genai
import os
import faiss
from openai import OpenAI
import json
import numpy as np
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Fixed hard carbon anode properties
V_ANODE_MIN = 0.01
V_ANODE_MAX = 2.0
V_ANODE_MID = 0.1       # plateau midpoint
C_SPEC_ANODE = 300      # mAh/g

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
csv_path = os.path.join(BASE_DIR, "..", "data", "CATHODES_DATASET.csv")
csv_path = os.path.abspath(csv_path)

# Load cathode dataset once (adjust path if needed)
df_base = pd.read_csv(csv_path)

genai.configure(
    api_key=os.environ.get("GOOGLE_API_KEY")
)
model = genai.GenerativeModel("gemini-2.5-flash")

# Rename columns after loading
df_base = df_base.rename(columns={
    "Practical Capacity (mAh/g)": "C_spec_cathode",
    "Voltage Window Lower (V)": "V_cathode_min",
    "Voltage Window Upper (V)": "V_cathode_max",
    "plateau voltage discharge": "V_cathode_mid"
})

def query_faiss_index(query_text, 
                      faiss_index_path=None,
                      metadata_path=None,
                      top_k=5):
    
    openai_api_key = os.getenv("OPENAI_API_KEY")
    if not openai_api_key:
        raise ValueError("OPENAI_API_KEY is not set")

    client = OpenAI(api_key=openai_api_key)

    BASE_DIR = os.path.dirname(os.path.abspath(__file__))

    if faiss_index_path is None:
        faiss_index_path = os.path.join(BASE_DIR, "../sentiment/faiss_index.idx")
    if metadata_path is None:
        metadata_path = os.path.join(BASE_DIR, "../sentiment/metadata.json")

    # Load index and metadata
    index = faiss.read_index(faiss_index_path)
    with open(metadata_path, "r", encoding="utf-8") as f:
        metadata = json.load(f)

    # Get embedding for query
    response = client.embeddings.create(
        input=query_text.lower(),
        model="text-embedding-3-large"
    )
    query_embedding = response.data[0].embedding
    query_embedding_np = np.array([query_embedding]).astype("float32")
    faiss.normalize_L2(query_embedding_np)

    # Search index
    distances, indices = index.search(query_embedding_np, top_k)
    results = []
    for dist, idx in zip(distances[0], indices[0]):
        meta = metadata[idx]
        results.append({
            "score": float(dist),
            "source_pdf": meta["source_pdf"],
            "page": meta["page"],
            "chunk_index": meta["chunk_index"],
            "text_snippet": meta["text"]
        })

    logger.info(f"Query: {query_text}")
    logger.info(f"Embedding length: {len(query_embedding)}")
    logger.info(f"Index dimension: {index.d}")
    logger.info(f"Index contains: {index.ntotal} vectors")
    logger.info(f"Distances: {distances}")
    logger.info(f"Indices: {indices}")
    logger.info(f"Metadata size: {len(metadata)}")
        
    return results

def calculate_capacity(cathode_name: str, L_anode: float, L_cathode: float,
                       M_total: float, t_total: float, C_rate: float):
    df = df_base.copy()
    df = df[df["Material_Name"] == cathode_name]

    if df.empty:
        raise ValueError(f"Cathode '{cathode_name}' not found in dataset.")

    df["C_spec_anode"] = C_SPEC_ANODE
    df["L_anode"] = L_anode
    df["L_cathode"] = L_cathode

    # Areal capacities
    df["Q_anode"] = C_SPEC_ANODE * L_anode / 1000
    df["Q_cathode"] = df["C_spec_cathode"] * L_cathode / 1000
    df["Q_full"] = df[["Q_anode", "Q_cathode"]].min(axis=1)

    # Base cell capacities
    df["Areal_Capacity_cell"] = df["Q_full"]
    df["Specific_Capacity_cell"] = df["Q_full"] / ((L_anode + L_cathode) / 1000)

    # Nominal voltage
    df["V_nominal"] = df["V_cathode_mid"] - V_ANODE_MID

    # Energy densities
    df["Gravimetric_Energy_Density"] = (1000 * df["Q_full"] * df["V_nominal"]) / M_total
    df["Volumetric_Energy_Density"] = (df["Q_full"] * df["V_nominal"] * 1000) / t_total

    # Voltage window
    df["V_cell_max"] = df["V_cathode_max"] - V_ANODE_MIN
    df["V_cell_min"] = df["V_cathode_min"] - V_ANODE_MAX
    df["Voltage_Window"] = df["V_cell_max"] - df["V_cell_min"]

    # Specific power
    df["Specific_Power"] = df["Gravimetric_Energy_Density"] * C_rate

    result = {
        "Cathode": cathode_name,
        "Anode": "Hard Carbon",
        "Q_areal": float(df["Q_full"].values[0]),
        "Q_specific": float(df["Specific_Capacity_cell"].values[0]),
        "Gravimetric_Energy_Density": float(df["Gravimetric_Energy_Density"].values[0]),
        "Volumetric_Energy_Density": float(df["Volumetric_Energy_Density"].values[0]),
        "V_nominal": float(df["V_nominal"].values[0]),
        "Voltage_Window": float(df["Voltage_Window"].values[0]),
        "Specific_Power": float(df["Specific_Power"].values[0])
    }

    query_text = (
        f"Sodium-ion battery with hard carbon anode and cathode {cathode_name}. "
    )

    faiss_results = query_faiss_index(query_text, top_k=5)

    try:
        prompt = f"""
        Remove the underscores and .pdf from the source_pdf field in the RAG section below and put it as References at the end of the explanation.
        You are to explain the results of the calculations of a sodium-ion full cell battery using hard carbon and {cathode_name}. You are a RAG system  that takes information only from the RAG section below.
        And respond with extensive/long explanation in a scientific way and straight to the point without any additional text. Do not include opinions just explanations
        of the results of the calculations.
        Lastly shortly analyze the performance of the full cell battery.

        Below are the formulas used to compute key metrics. After reviewing them, interpret the calculated results:

        **Fixed Anode Parameters:**
        - Specific Capacity of Anode (C_SPEC_ANODE): 300 mAh/g
        - Voltage Window: 0.01 V to 2.0 V
        - Plateau Midpoint Voltage (V_ANODE_MID): 0.1 V

        **Formulas Used:**

        1. **Areal Capacity (mAh/cm²):**
        - Q_anode = C_SPEC_ANODE × L_anode / 1000  
        - Q_cathode = C_spec_cathode × L_cathode / 1000  
        - Q_full = min(Q_anode, Q_cathode)

        2. **Specific Capacity (mAh/g):**  
        - Q_specific = Q_full / ((L_anode + L_cathode) / 1000)

        3. **Nominal Voltage (V):**
        - V_nominal = V_cathode_mid - V_ANODE_MID

        4. **Gravimetric Energy Density (Wh/kg):**  
        - GED = 1000 × Q_full × V_nominal / M_total

        5. **Volumetric Energy Density (Wh/L):**  
        - VED = Q_full × V_nominal × 1000 / t_total

        6. **Voltage Window (V):**  
        - V_cell_max = V_cathode_max - 0.01  
        - V_cell_min = V_cathode_min - 2.0  
        - Voltage_Window = V_cell_max - V_cell_min

        7. **Specific Power (W/kg):**
        - P = V_nominal × Q_full × 1000 × C_rate / 3600

        **Inputs Provided:**
        - Cathode material: {cathode_name}
        - L_anode (mg/cm²): {L_anode}
        - L_cathode (mg/cm²): {L_cathode}
        - M_total (mg): {M_total}
        - t_total (mm): {t_total}
        - C_rate: {C_rate}

        **Calculated Results:**
        ```json
        {result }
        ```
        Remove the underscores and .pdf from the source_pdf field in the RAG section below and put it as References at the end of the explanation. Do not include the PDF file name directly in the explanation.
        At the end of the explanation, list the full source_pdf file names used as references.
        RAG Section, use only the information from this section to explain the results:
        {json.dumps(faiss_results, indent=2)}
        """
        logger.info("Generated prompt for Gemini model: %s", prompt.strip())
        gemini_response = model.generate_content(prompt)

        if gemini_response.candidates:
            parts = gemini_response.candidates[0].content.parts
            explanation = " ".join(
                p.text for p in parts if hasattr(p, "text") and p.text
            )
        else:
            explanation = "Gemini returned no candidates."

        result["Gemini_Explanation"] = explanation.strip()

    except Exception as e:
        result["Gemini_Explanation"] = f"Gemini error: {str(e)}"
   

    return result