Create app.py

app.py

@@ -0,0 +1,191 @@
+import streamlit as st
+import base64, tempfile, requests, os, re, json
+import whisper  # local Whisper model
+from piper import PiperVoice
+import wave
+
+from sentence_transformers import SentenceTransformer, util  # NEW for embeddings retrieval
+
+
+# ─────────────────────────────────────────────────────────────
+st.set_page_config(page_title="Med11 – AI Medical Emergency guide", page_icon="🩺")
+
+GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
+
+st.session_state.setdefault("show_numbers", False)
+st.session_state.setdefault("upload_id", 0)
+st.session_state.setdefault("chat_history", [])
+
+# ─────────────────────────────────────────────────────────────
+@st.cache_resource(show_spinner="Loading Whisper model…")
+def _load_whisper():
+    return whisper.load_model("base")
+
+@st.cache_resource(show_spinner="Loading Piper voice model…")
+def load_voice():
+    return PiperVoice.load("models/en_US-amy-low.onnx")
+
+# ─────────────── NEW: Load and embed first aid JSON knowledge ───────────────
+@st.cache_resource(show_spinner="Loading embedder and first aid data…")
+def load_embedder_and_knowledge():
+    embedder = SentenceTransformer("all-MiniLM-L6-v2")
+    # Load JSON knowledge base (adjust path if needed)
+    with open("first_aid_intents.json", "r", encoding="utf-8") as f:
+        intents = json.load(f)
+    # Flatten all patterns+responses as docs
+    docs = []
+    for intent in intents:
+        for p in intent.get("patterns", []):
+            docs.append(p)
+        for r in intent.get("responses", []):
+            docs.append(r)
+    embeddings = embedder.encode(docs, convert_to_tensor=True)
+    return embedder, docs, embeddings
+
+embedder, docs, embeddings = load_embedder_and_knowledge()
+
+def get_relevant_context(query, top_k=3):
+    query_emb = embedder.encode(query, convert_to_tensor=True)
+    hits = util.semantic_search(query_emb, embeddings, top_k=top_k)[0]
+    return "\n".join(docs[hit["corpus_id"]] for hit in hits)
+
+# ─────────────────────────────────────────────────────────────
+def groq_reply(prompt: str, model="llama3-8b-8192"):
+    # ───── NEW: Retrieve local first aid context ─────
+    context = get_relevant_context(prompt)
+    full_prompt = (
+        f"Use the following first-aid information to answer accurately:\n\n{context}\n\n"
+        f"Emergency query: {prompt}"
+    )
+
+    url = "https://api.groq.com/openai/v1/chat/completions"
+    payload = {
+        "model": model,
+        "messages": [
+            {
+                "role": "system",
+                "content": (
+                    "You are an emergency-response medic. "
+                    "Classify the emergency, rate severity, and give clear, step-by-step first-aid. "
+                    "Limit to 120 words."
+                ),
+            },
+            *st.session_state.chat_history,
+            {"role": "user", "content": full_prompt},  # ← uses augmented prompt
+        ],
+        "temperature": 0.2,
+        "max_tokens": 256,
+    }
+    r = requests.post(
+        url, json=payload,
+        headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
+        timeout=45,
+    )
+    r.raise_for_status()
+    return r.json()["choices"][0]["message"]["content"]
+
+def transcribe_audio(file_path: str) -> str:
+    model = _load_whisper()
+    result = model.transcribe(file_path)
+    return result["text"]
+
+def clean_text_for_tts(text: str) -> str:
+    text = re.sub(r"\*\*|\*|__|_", "", text)
+    text = re.sub(r"[|~>#]", "", text)
+    text = re.sub(r"[\u2600-\u26FF\u2700-\u27BF]+", "", text)
+    text = re.sub(r"\s+", " ", text)
+    return text.strip()
+
+def tts_player(text: str):
+    voice = load_voice()
+    clean_text = clean_text_for_tts(text)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_wav:
+        with wave.open(tmp_wav.name, "wb") as wav_file:
+            wav_file.setnchannels(1)
+            wav_file.setsampwidth(2)
+            wav_file.setframerate(voice.config.sample_rate)
+            voice.synthesize(clean_text, wav_file)
+
+        audio_bytes = open(tmp_wav.name, "rb").read()
+
+    st.audio(audio_bytes, format="audio/wav")
+    os.remove(tmp_wav.name)
+
+
+st.title("🩺 Med11 – AI Medical Emergency guide")
+
+if st.button("📞 Emergency Numbers"):
+    st.session_state.show_numbers = not st.session_state.show_numbers
+
+if st.session_state.show_numbers:
+    with st.expander("Key Emergency Numbers (Pakistan)", expanded=True):
+        st.markdown(
+            """
+            | Service | Number |
+            |---------|--------|
+            | 🚑 **Edhi Ambulance** | **115** |
+            | 🚒 **Rescue / Fire** | **1122** |
+            | 🚓 Police | **15** |
+            | 💊 Poison Control (NIH) | **051-9255075** |
+            """
+        )
+
+uploader_key = f"audio_{st.session_state.upload_id}"
+audio_file = st.file_uploader(
+    "Upload a WAV/MP3 describing the emergency",
+    type=["wav", "mp3", "m4a"],
+    key=uploader_key,
+)
+
+if audio_file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
+        tmp.write(audio_file.read())
+        tmp_path = tmp.name
+
+    with st.spinner("Transcribing…"):
+        try:
+            transcript = transcribe_audio(tmp_path).strip()
+        except Exception as e:
+            st.error(f"Transcription error: {e}")
+            transcript = ""
+    os.remove(tmp_path)
+
+    with st.expander("📜 Transcript", expanded=False):
+        st.write(transcript or "_(no speech detected)_")
+
+    if transcript:
+        with st.spinner("Analyzing…"):
+            try:
+                result = groq_reply(
+                    f'Analyze this emergency: "{transcript}". '
+                    "Classify type, rate severity, and give brief first-aid steps."
+                )
+                st.session_state.chat_history.append({"role": "user", "content": transcript})
+                st.session_state.chat_history.append({"role": "assistant", "content": result})
+            except Exception as e:
+                st.error(f"Groq error: {e}")
+                result = ""
+
+        if result:
+            tts_player(result)
+            st.markdown("### 🧠 Guidance")
+            st.markdown(result)
+
+            st.session_state.upload_id += 1
+            st.markdown("---")
+            st.markdown("**Need to tell me more? Upload another voice clip below.**")
+
+col1, col2 = st.columns(2)
+with col1:
+    if st.button("🏥 Nearest Hospital (Google Maps)"):
+        st.markdown(
+            "[Open Maps →](https://www.google.com/maps/search/hospital/)",
+            unsafe_allow_html=True,
+        )
+with col2:
+    if st.button("🗺️ Nearby Pharmacies"):
+        st.markdown(
+            "[Open Maps →](https://www.google.com/maps/search/pharmacy/)",
+            unsafe_allow_html=True,
+        )