Create app.py

app.py

@@ -0,0 +1,137 @@
+import streamlit as st
+import os
+import json
+import faiss
+import numpy as np
+from vosk import Model, KaldiRecognizer
+import wave
+from pydub import AudioSegment
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+# ------------------------------
+# 1. LOAD MODELS OFFLINE
+# ------------------------------
+
+@st.cache_resource
+def load_stt_model():
+    model_path = "vosk-model-small-en-us-0.15"
+    return Model(model_path)
+
+@st.cache_resource
+def load_embedding_model():
+    return SentenceTransformer("all-MiniLM-L6-v2")
+
+@st.cache_resource
+def load_qa_model():
+    return pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+
+stt_model = load_stt_model()
+embedder = load_embedding_model()
+qa_model = load_qa_model()
+
+# ------------------------------
+# 2. FUNCTIONS
+# ------------------------------
+
+def transcribe_audio(file_path):
+    wf = wave.open(file_path, "rb")
+    rec = KaldiRecognizer(stt_model, wf.getframerate())
+    rec.SetWords(True)
+
+    text_result = ""
+
+    while True:
+        data = wf.readframes(4000)
+        if len(data) == 0:
+            break
+        if rec.AcceptWaveform(data):
+            res = rec.Result()
+            text_result += json.loads(res)["text"] + " "
+
+    final_res = rec.FinalResult()
+    text_result += json.loads(final_res)["text"]
+
+    return text_result.strip()
+
+
+def convert_to_wav(uploaded_file):
+    audio = AudioSegment.from_file(uploaded_file)
+    output_path = "temp.wav"
+    audio.export(output_path, format="wav")
+    return output_path
+
+
+def save_text(text):
+    with open(f"transcripts/data.txt", "a", encoding="utf-8") as f:
+        f.write(text + "\n")
+
+
+def build_vector_db():
+    with open("transcripts/data.txt", "r", encoding="utf-8") as f:
+        docs = f.readlines()
+
+    embeddings = embedder.encode(docs)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings).astype("float32"))
+    return docs, index
+
+
+def retrieve(query, docs, index, top_k=3):
+    q_emb = embedder.encode([query])
+    D, I = index.search(np.array(q_emb).astype("float32"), top_k)
+    results = [docs[i] for i in I[0]]
+    return " ".join(results)
+
+
+# ------------------------------
+# 3. STREAMLIT UI
+# ------------------------------
+
+st.title("🔴 Offline GenAI RAG from Audio (No API • No Internet)")
+st.write("🎤 Upload or record audio → 📄 Convert to text → 🤖 Ask questions offline")
+
+menu = st.sidebar.radio("Navigation", ["Upload Audio", "Ask Questions"])
+
+# -----------------------------------
+#  UPLOAD AUDIO PAGE
+# -----------------------------------
+if menu == "Upload Audio":
+    st.header("🎤 Upload or Record Audio")
+
+    audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3", "m4a"])
+
+    if audio_file:
+        st.success("File uploaded successfully")
+
+        wav_path = convert_to_wav(audio_file)
+
+        st.info("Transcribing offline... please wait")
+
+        text = transcribe_audio(wav_path)
+
+        st.subheader("📝 Transcribed Text")
+        st.write(text)
+
+        save_text(text)
+
+        st.success("Saved locally in transcripts/ folder")
+
+# -----------------------------------
+#  ASK QUESTION PAGE
+# -----------------------------------
+if menu == "Ask Questions":
+    st.header("❓ Ask Questions From Your Audio Knowledge Base")
+
+    docs, index = build_vector_db()
+
+    user_q = st.text_input("Enter your question")
+
+    if st.button("Get Answer"):
+        context = retrieve(user_q, docs, index)
+        result = qa_model(question=user_q, context=context)
+
+        st.subheader("🧠 Answer")
+        st.write(result["answer"])
+
+        st.caption("Based only on your stored audio transcriptions")