main.py

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
import os
import openai
from io import BytesIO
from gtts import gTTS
import tempfile
from dotenv import load_dotenv
from sentence_transformers import SentenceTransformer
import math
from collections import Counter
import json
import pandas as pd
import asyncio
import numpy as np
from deepgram import Deepgram 
from fastapi.staticfiles import StaticFiles

load_dotenv()
DEEPGRAM_API_KEY = os.getenv("DEEPGRAM_API_KEY")  # Add this to your .env
dg_client = Deepgram(DEEPGRAM_API_KEY)  # Initialize Deepgram client
openai.api_key = os.getenv("OPENAI_API_KEY")

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app.mount("/static", StaticFiles(directory="static"), name="static")

@app.get("/", response_class=HTMLResponse)
async def serve_html():
    with open("templates/index.html", "r", encoding="utf-8") as f:
        html_content = f.read()
    return HTMLResponse(content=html_content)


chat_messages = [{"role": "system", "content": '''
        You are kammi, a friendly, human-like voice assistant built by Facile AI Solutions, headed by Deepti.You assist customers specifically with knee replacement surgery queries and you are the assistant of Dr.Sandeep who is a highly experienced knee replacement surgeon.

        Rules for your responses:

        1. **Context-driven answers only**: Answer strictly based on the provided context and previous conversation history. Do not use external knowledge. 

        2. **General conversation**: Engage in greetings and casual conversation. If the user mentions their name, greet them personally and continue using their name.

        3. **Technical/medical queries**:
        - If the question is **relevant to knee replacement surgery** and the answer is in the context or chat history, provide the answer.
        - If the question is **relevant but not present in the context**, respond: "please connect with Dr.Sandeep or Reception for this details."

        4. **Irrelevant queries**:
        - If the question is completely unrelated to knee replacement surgery, politely decline and respond: "I am here to assist only with knee replacement surgery related queries."

        5. **Drive conversation**:
        - After answering the user’s question, suggest a follow-up question from the context that you can answer. 
        - Make the follow-up natural and conversational. The follow up question must be relevant to the current question or response
        - If the user responds with confirmation like “yes”, “okay” give the answer for the previous follow-up question from the context.

        6. **Readable voice output for gTTS**:
        - Break sentences at natural punctuation: `, . ? ! : ;`.
        - Do not use `#`, `**`, or other markdown symbols.
        - Numbers and points must be spelled out: e.g., `2.5 lakh` → `two point five lakh`. Similarly Dr, Mr, Mrs, etc. must be written as Doctor, Mister, Misses etc. 
        7. **Concise and human-like**:
        - Keep answers short, conversational, and natural.
        - Maximum 40 words / ~20 seconds of speech.

        8. **Tone and style**:
        - Helpful, friendly, approachable, and human-like.
        - Maintain professionalism while being conversational.
        
        9. **About Dr.Sandeep**:
        - He has over 15 years of experience in orthopedic and joint replacement surgery.
        - He specializes in total and partial knee replacement procedures.
        - Known for a patient-friendly approach, focusing on pre-surgery preparation, post-surgery rehabilitation, and pain management.
        - Actively keeps up-to-date with the latest techniques and technologies in knee replacement surgery.
        - Highly approachable and prefers that patients are well-informed about their treatment options and recovery process.

        Always provide readable, streaming-friendly sentences so gTTS can read smoothly. Drive conversation forward while staying strictly on knee replacement surgery topics, and suggest follow-up questions for which you have context-based answers.
'''}]

class BM25:
    def __init__(self, corpus, k1=1.2, b=0.75):
        self.corpus = [doc.split() if isinstance(doc, str) else doc for doc in corpus]
        self.k1 = k1
        self.b = b
        self.N = len(self.corpus)
        self.avgdl = sum(len(doc) for doc in self.corpus) / self.N
        self.doc_freqs = self._compute_doc_frequencies()
        self.idf = self._compute_idf()

    def _compute_doc_frequencies(self):
        """Count how many documents contain each term"""
        df = {}
        for doc in self.corpus:
            unique_terms = set(doc)
            for term in unique_terms:
                df[term] = df.get(term, 0) + 1
        return df

    def _compute_idf(self):
        """Compute the IDF for each term in the corpus"""
        idf = {}
        for term, df in self.doc_freqs.items():
            idf[term] = math.log((self.N - df + 0.5) / (df + 0.5) + 1)
        return idf

    def score(self, query, document):
        """Compute the BM25 score for one document and one query"""
        query_terms = query.split() if isinstance(query, str) else query
        doc_terms = document.split() if isinstance(document, str) else document
        score = 0.0
        freqs = Counter(doc_terms)
        doc_len = len(doc_terms)

        for term in query_terms:
            if term not in freqs:
                continue
            f = freqs[term]
            idf = self.idf.get(term, 0)
            denom = f + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl)
            score += idf * (f * (self.k1 + 1)) / denom
        return score

    def rank(self, query):
        """Rank all documents for a given query"""
        return [(i, self.score(query, doc)) for i, doc in enumerate(self.corpus)]


def sigmoid_scaled(x, midpoint=3.0):
    """
    Sigmoid function with shifting.
    `midpoint` controls where the output is 0.5.
    """
    return 1 / (1 + math.exp(-(x - midpoint)))

def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:

    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

async def compute_similarity(query: str, query_embedding: np.ndarray, chunk_text: str, chunk_embedding: np.ndarray, sem_weight: float,syn_weight:float,bm25) -> float:

    semantic_score = cosine_similarity(query_embedding, chunk_embedding)

    # syntactic_score = fuzz.ratio(query, chunk_text) / 100.0
    syntactic_score = bm25.score(query,chunk_text)
    final_syntactic_score = sigmoid_scaled(syntactic_score)

    combined_score = sem_weight * semantic_score + syn_weight * final_syntactic_score

    return combined_score

async def retrieve_top_k_hybrid(query, k, sem_weight,syn_weight,bm25):

    query_embedding = model.encode(query)

    tasks = [

        compute_similarity(query, query_embedding, row["Chunks"], row["Embeddings"] , sem_weight,syn_weight,bm25)

        for _, row in df_expanded.iterrows()

    ]

    similarities = await asyncio.gather(*tasks)

    df_expanded["similarity"] = similarities

    top_results = df_expanded.sort_values(by="similarity", ascending=False).head(k)

    return top_results["Chunks"].to_list()

model = SentenceTransformer("abhinand/MedEmbed-large-v0.1")
df_expanded = pd.read_excel("Database.xlsx")  # Replace with your filename
df_expanded["Embeddings"] = df_expanded["Embeddings"].map(lambda x: json.loads(x))
corpus = df_expanded['Chunks'].to_list()
bm25 = BM25(corpus)


# --- gTTS helper: stream raw audio file in small chunks ---
def tts_chunk_stream(text_chunk: str, lang: str = "en"):
    if not text_chunk.strip():
        return []

    tts = gTTS(text=text_chunk, lang=lang)
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
    tts.save(temp_file.name)

    def audio_stream():
        try:
            with open(temp_file.name, "rb") as f:
                chunk = f.read(1024)
                while chunk:
                    yield chunk
                    chunk = f.read(1024)
        finally:
            try:
                os.remove(temp_file.name)
            except Exception:
                pass

    return audio_stream()

async def get_rag_response(user_message: str):
    global chat_messages
    Chunks = await retrieve_top_k_hybrid(user_message,15, 0.9, 0.1,bm25)
    context = "======================================================================================================\n".join(Chunks)
    chat_messages.append({"role": "user", "content": f'''     
          Context : {context}
          User Query: {user_message}'''})
    # print("chat_messages",chat_messages)
    # response = get_response(query, context, chat_messages)
    # chat_messages.append({"role": "assistant", "content": response})
    return chat_messages


# --- GPT + TTS async generator with smaller buffer like second code ---
async def gpt_tts_stream(prompt: str):
    print("started gpt_tts_stream",prompt)
    global chat_messages
    chat_messages = await get_rag_response(prompt)
    # print(chat_messages,"chat_messages after getting RAG response")
    response = openai.ChatCompletion.create(
        model="gpt-4o",
        messages= chat_messages,
        stream=True
    )
    buffer = ""
    BUFFER_SIZE = 20  # smaller buffer like second code
    bot_response = ""

    for chunk in response:
        choices = chunk.get("choices", [])
        if not choices:
            continue

        delta = choices[0]["delta"].get("content", "")
        finish_reason = choices[0].get("finish_reason")
        if delta:
            bot_response = bot_response + delta
            buffer += delta
            if len(buffer) >= BUFFER_SIZE and buffer.endswith((".", "!",",", "?", "\n", ";", ":")):
                for audio_chunk in tts_chunk_stream(buffer):
                    print("chunk",buffer)
                    yield audio_chunk
                    buffer = ""

        if finish_reason is not None:
            break
    
    bot_response = bot_response.strip()
    chat_messages.append({"role": "assistant", "content": bot_response})

    if buffer.strip():
        for audio_chunk in tts_chunk_stream(buffer):
            yield audio_chunk

@app.post("/chat_stream")
async def chat_stream(file: UploadFile = File(...)):
    audio_bytes = await file.read()

    # Transcribe using Deepgram
    response = await dg_client.transcription.prerecorded(
        {
            "buffer": audio_bytes,
            "mimetype": "audio/webm" 
        },
        {
            "model": "nova-3",  
            "language": "en",
            "punctuate": True,
            "smart_format": True
        }
    )

    transcript_text = response["results"]["channels"][0]["alternatives"][0]["transcript"].strip()

    return StreamingResponse(gpt_tts_stream(transcript_text), media_type="audio/mpeg")


@app.post("/reset_chat")
async def reset_chat():
    global chat_messages
    chat_messages = [{
        "role": "system",
        "content": '''
        You are kammi, a friendly, human-like voice assistant built by Facile AI Solutions, headed by Deepti.You assist customers specifically with knee replacement surgery queries and you are the assistant of Dr.Sandeep who is a highly experienced knee replacement surgeon.

        Rules for your responses:

        1. **Context-driven answers only**: Answer strictly based on the provided context and previous conversation history. Do not use external knowledge. 

        2. **General conversation**: Engage in greetings and casual conversation. If the user mentions their name, greet them personally and continue using their name.

        3. **Technical/medical queries**:
        - If the question is **relevant to knee replacement surgery** and the answer is in the context or chat history, provide the answer.
        - If the question is **relevant but not present in the context**, respond: "please connect with Dr.Sandeep or Reception for this details."

        4. **Irrelevant queries**:
        - If the question is completely unrelated to knee replacement surgery, politely decline and respond: "I am here to assist only with knee replacement surgery related queries."

        5. **Drive conversation**:
        - After answering the user’s question, suggest a follow-up question from the context that you can answer. 
        - Make the follow-up natural and conversational. The follow up question must be relevant to the current question or response
        - If the user responds with confirmation like “yes”, “okay” give the answer for the previous follow-up question from the context.

        6. **Readable voice output for gTTS**:
        - Break sentences at natural punctuation: `, . ? ! : ;`.
        - Do not use `#`, `**`, or other markdown symbols.
        - Numbers and points must be spelled out: e.g., `2.5 lakh` → `two point five lakh`. Similarly Dr, Mr, Mrs, etc. must be written as Doctor, Mister, Misses etc. 
        7. **Concise and human-like**:
        - Keep answers short, conversational, and natural.
        - Maximum 40 words / ~20 seconds of speech.

        8. **Tone and style**:
        - Helpful, friendly, approachable, and human-like.
        - Maintain professionalism while being conversational.

        9. **About Dr.Sandeep**:
        - He has over 15 years of experience in orthopedic and joint replacement surgery.
        - He specializes in total and partial knee replacement procedures.
        - Known for a patient-friendly approach, focusing on pre-surgery preparation, post-surgery rehabilitation, and pain management.
        - Actively keeps up-to-date with the latest techniques and technologies in knee replacement surgery.
        - Highly approachable and prefers that patients are well-informed about their treatment options and recovery process.

        Always provide readable, streaming-friendly sentences so gTTS can read smoothly. Drive conversation forward while staying strictly on knee replacement surgery topics, and suggest follow-up questions for which you have context-based answers.
'''
    }]
    return {"message": "Chat history reset successfully."}