Update app.py

app.py

@@ -1,139 +1,195 @@
-import gradio as gr
-import speech_recognition as sr
-from time import time
-import threading
-from pydub import AudioSegment
-from pydub.playback import play
-import io
-
-# Global variables
-is_recording = False
-start_beep = AudioSegment.silent(duration=200).append(AudioSegment.from_wav(io.BytesIO(b''), crossfade=100)
-end_beep = AudioSegment.silent(duration=200).append(AudioSegment.from_wav(io.BytesIO(b'')), crossfade=100)
-
-def play_start_sound():
-    try:
-        play(start_beep)
-    except:
-        pass
+import streamlit as st
+import time
+import requests
+from streamlit.components.v1 import html
+import os
+from dotenv import load_dotenv
 
-def play_end_sound():
-    try:
-        play(end_beep)
-    except:
-        pass
-
-def start_recording(audio_time_limit):
-    global is_recording
-    is_recording = True
-    recognizer = sr.Recognizer()
-    microphone = sr.Microphone()
-    
-    play_start_sound()
-    
-    with microphone as source:
-        recognizer.adjust_for_ambient_noise(source, duration=0.5)
-        try:
-            audio = recognizer.listen(source, timeout=3, phrase_time_limit=audio_time_limit)
-            text = recognizer.recognize_google(audio)
-            return text
-        except sr.WaitTimeoutError:
-            return ""
-        except sr.UnknownValueError:
-            return ""
-        except Exception as e:
-            print(f"Error: {str(e)}")
-            return ""
-        finally:
-            play_end_sound()
-            is_recording = False
-
-def transcribe_audio(audio_time_limit=10):
-    def execute_recording():
-        nonlocal result
-        result = start_recording(audio_time_limit)
-    
-    result = ""
-    recording_thread = threading.Thread(target=execute_recording)
-    recording_thread.start()
-    
-    start_time = time()
-    while is_recording and (time() - start_time) < audio_time_limit:
-        time_elapsed = time() - start_time
-        time_left = max(0, audio_time_limit - time_elapsed)
-        progress = 1 - (time_left / audio_time_limit)
-        yield {"__type__": "update", "value": f"🎤 Recording... {time_left:.1f}s left", "visible": True}, {"__type__": "update", "value": "", "visible": True}
-        gr.sleep(0.1)
-    
-    recording_thread.join()
-    yield {"__type__": "update", "value": "✅ Done!", "visible": True}, {"__type__": "update", "value": result, "visible": True}
-
-def create_ui():
-    css = """
-    .mic-button {
-        background: linear-gradient(45deg, #FF3366, #BA265D) !important;
-        border: none !important;
-        color: white !important;
-        padding: 12px !important;
-        border-radius: 50% !important;
-        height: 50px !important;
-        width: 50px !important;
-        margin-left: 10px !important;
-    }
-    .mic-button:hover {
-        transform: scale(1.05) !important;
-    }
-    .input-with-mic {
-        display: flex !important;
-        align-items: center !important;
-        gap: 10px !important;
+# Voice input dependencies
+import torchaudio
+import numpy as np
+import torch
+from io import BytesIO
+import hashlib
+from audio_recorder_streamlit import audio_recorder
+from transformers import pipeline
+
+######################################
+# Voice Input Helper Functions
+######################################
+
+@st.cache_resource
+def load_voice_model():
+    return pipeline("automatic-speech-recognition", model="openai/whisper-base")
+
+def process_audio(audio_bytes):
+    waveform, sample_rate = torchaudio.load(BytesIO(audio_bytes))
+    if waveform.shape[0] > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    if sample_rate != 16000:
+        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+        waveform = resampler(waveform)
+    return {"raw": waveform.numpy().squeeze(), "sampling_rate": 16000}
+
+def get_voice_transcription(state_key):
+    if state_key not in st.session_state:
+        st.session_state[state_key] = ""
+    audio_bytes = audio_recorder(
+        key=state_key + "_audio",
+        pause_threshold=0.8,
+        text="🎙️ Speak your message",
+        recording_color="#e8b62c",
+        neutral_color="#6aa36f"
+    )
+    if audio_bytes:
+        current_hash = hashlib.md5(audio_bytes).hexdigest()
+        last_hash_key = state_key + "_last_hash"
+        if st.session_state.get(last_hash_key, "") != current_hash:
+            st.session_state[last_hash_key] = current_hash
+            try:
+                audio_input = process_audio(audio_bytes)
+                whisper = load_voice_model()
+                transcribed_text = whisper(audio_input)["text"]
+                st.info(f"📝 Transcribed: {transcribed_text}")
+                st.session_state[state_key] += (" " + transcribed_text).strip()
+                st.experimental_rerun()
+            except Exception as e:
+                st.error(f"Voice input error: {str(e)}")
+    return st.session_state[state_key]
+
+######################################
+# Game Functions & Styling
+######################################
+
+@st.cache_resource
+def get_help_agent():
+    return pipeline("conversational", model="facebook/blenderbot-400M-distill")
+
+def inject_custom_css():
+    st.markdown("""
+    <style>
+        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
+        * { font-family: 'Inter', sans-serif; }
+        .title { font-size: 2.8rem !important; font-weight: 800 !important;
+                 background: linear-gradient(45deg, #6C63FF, #3B82F6);
+                 -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+                 text-align: center; margin: 1rem 0; }
+        .subtitle { font-size: 1.1rem; text-align: center; color: #64748B; margin-bottom: 2.5rem; }
+        .question-box { background: white; border-radius: 20px; padding: 2rem; margin: 1.5rem 0;
+                        box-shadow: 0 10px 25px rgba(0,0,0,0.08); border: 1px solid #e2e8f0; color: black; }
+        .input-box { background: white; border-radius: 12px; padding: 1.5rem; margin: 1rem 0;
+                     box-shadow: 0 4px 6px rgba(0,0,0,0.05); }
+        .stTextInput input { border: 2px solid #e2e8f0 !important; border-radius: 10px !important;
+                              padding: 12px 16px !important; }
+        button { background: linear-gradient(45deg, #6C63FF, #3B82F6) !important;
+                 color: white !important; border-radius: 10px !important;
+                 padding: 12px 24px !important; font-weight: 600; }
+        .final-reveal { font-size: 2.8rem;
+                        background: linear-gradient(45deg, #6C63FF, #3B82F6);
+                        -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+                        text-align: center; margin: 2rem 0; font-weight: 800; }
+    </style>
+    """, unsafe_allow_html=True)
+
+def show_confetti():
+    html("""
+    <canvas id="confetti-canvas" class="confetti"></canvas>
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/confetti.browser.min.js"></script>
+    <script>
+    const count = 200;
+    const defaults = { origin: { y: 0.7 }, zIndex: 1050 };
+    function fire(particleRatio, opts) {
+        confetti(Object.assign({}, defaults, opts, {
+            particleCount: Math.floor(count * particleRatio)
+        }));
     }
-    .status-message {
-        font-style: italic;
-        color: #666;
-        margin-top: 5px;
+    fire(0.25, { spread: 26, startVelocity: 55 });
+    fire(0.2, { spread: 60 });
+    fire(0.35, { spread: 100, decay: 0.91, scalar: 0.8 });
+    fire(0.1, { spread: 120, startVelocity: 25, decay: 0.92, scalar: 1.2 });
+    fire(0.1, { spread: 120, startVelocity: 45 });
+    </script>
+    """)
+
+def ask_llama(conversation_history, category, is_final_guess=False):
+    api_url = "https://api.groq.com/openai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {os.getenv('GROQ_API_KEY')}",
+        "Content-Type": "application/json"
     }
-    """
+    system_prompt = f"""You're playing 20 questions to guess a {category}. Rules:
+1. Ask strategic, non-repeating yes/no questions to narrow down.
+2. Use all previous answers smartly.
+3. If you're 80%+ sure, say: Final Guess: [your guess]
+4. For places: ask about continent, country, landmarks, etc.
+5. For people: ask if real, profession, gender, etc.
+6. For objects: ask about use, size, material, etc."""
     
-    with gr.Blocks(css=css) as demo:
-        gr.Markdown("## 🎤 Speech to Text Converter")
-        
-        with gr.Group():
-            with gr.Row():
-                text_input = gr.Textbox(
-                    label="Your Input",
-                    placeholder="Click the mic button and speak...",
-                    elem_classes=["input-box"],
-                    scale=9
-                )
-                mic_button = gr.Button(
-                    "🎤",
-                    elem_classes=["mic-button"],
-                    scale=1
-                )
-            
-            status_display = gr.Textbox(
-                label="Status",
-                visible=False,
-                interactive=False,
-                elem_classes=["status-message"]
-            )
-        
-        mic_button.click(
-            fn=transcribe_audio,
-            inputs=[gr.Slider(5, 30, value=10, label="Recording time limit (seconds)")],
-            outputs=[status_display, text_input],
-            show_progress="hidden"
-        )
-        
-        gr.Examples(
-            examples=["Hello world", "How are you today?", "Please convert my speech to text"],
-            inputs=text_input,
-            label="Try these examples:"
-        )
+    prompt = f"""Based on these answers about a {category}, provide ONLY your final guess with no extra text:
+{conversation_history}""" if is_final_guess else "Ask your next smart yes/no question."
+
+    messages = [{"role": "system", "content": system_prompt}]
+    messages += conversation_history
+    messages.append({"role": "user", "content": prompt})
+
+    data = {
+        "model": "llama-3-70b-8192",
+        "messages": messages,
+        "temperature": 0.8,
+        "max_tokens": 100
+    }
+
+    try:
+        res = requests.post(api_url, headers=headers, json=data)
+        res.raise_for_status()
+        return res.json()["choices"][0]["message"]["content"]
+    except Exception as e:
+        st.error(f"❌ LLaMA API error: {e}")
+        return "..."
+
+######################################
+# Main App Logic Here (UI, Game Loop)
+######################################
+
+def main():
+    load_dotenv()
+    inject_custom_css()
+
+    st.title("🎮 Guess It! - 20 Questions Game")
+    st.markdown("<div class='subtitle'>Think of a person, place, or object. LLaMA will try to guess it!</div>", unsafe_allow_html=True)
+
+    category = st.selectbox("Category of your secret:", ["Person", "Place", "Object"])
     
-    return demo
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = []
+        st.session_state.last_bot_msg = ""
+
+    if st.button("🔄 Restart Game"):
+        st.session_state.conversation = []
+        st.session_state.last_bot_msg = ""
+        st.rerun()
+
+    if not st.session_state.conversation:
+        st.session_state.last_bot_msg = ask_llama([], category)
+        st.session_state.conversation.append({"role": "assistant", "content": st.session_state.last_bot_msg})
+
+    st.markdown(f"<div class='question-box'><strong>LLaMA:</strong> {st.session_state.last_bot_msg}</div>", unsafe_allow_html=True)
+
+    user_input = get_voice_transcription("voice_input") or st.text_input("💬 Your answer (yes/no/sometimes):")
+
+    if st.button("Submit Answer") and user_input:
+        st.session_state.conversation.append({"role": "user", "content": user_input})
+        with st.spinner("Thinking..."):
+            response = ask_llama(st.session_state.conversation, category)
+        st.session_state.last_bot_msg = response
+        st.session_state.conversation.append({"role": "assistant", "content": response})
+        st.rerun()
+
+    if st.button("🤔 Make Final Guess"):
+        with st.spinner("Making final guess..."):
+            final_guess = ask_llama(st.session_state.conversation, category, is_final_guess=True)
+            st.markdown(f"<div class='final-reveal'>🤯 Final Guess: <strong>{final_guess}</strong></div>", unsafe_allow_html=True)
+            show_confetti()
 
 if __name__ == "__main__":
-    demo = create_ui()
-    demo.launch(debug=True)
+    main()