Upload app.py

app.py

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+import gradio as gr
+import whisper
+import torch
+from transformers import pipeline
+import warnings
+warnings.filterwarnings("ignore")
+
+
+print("Loading Whisper (transcription)...")
+whisper_model = whisper.load_model("base")
+
+model_name = "Qwen/Qwen2.5-0.5B-Instruct"
+print(f"Loading {model_name} (reflection)...")
+generator = pipeline(
+    "text-generation",
+    model=model_name,
+    device_map="auto",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+)
+
+print("Models ready.")
+
+def transcribe(audio_path: str) -> str:
+    """Transcribe audio file to text using Whisper."""
+    if audio_path is None:
+        return ""
+    result = whisper_model.transcribe(audio_path)
+    return result["text"].strip()
+
+
+def reflect(transcript: str) -> str:
+    """Generate a journaling reflection using Qwen."""
+    if not transcript:
+        return "No transcript found — try recording again."
+
+    prompt = f"""You are a warm, thoughtful journaling companion. Your tone is human and gentle, never clinical or robotic.
+
+The user just shared this voice journal entry:
+
+"{transcript}"
+
+Respond with exactly three parts, clearly separated:
+
+**Mood check:** A 2-sentence summary of the emotional tone you picked up.
+
+**What I noticed:** One pattern, theme, or detail that stood out in what they shared.
+
+**Something to sit with:** One gentle, open question for them to reflect on later.
+
+Keep the full response under 160 words. Be kind, specific, and real."""
+
+    messages = [
+        {"role": "system", "content": "You are a warm journaling companion. Be human, brief, and specific."},
+        {"role": "user", "content": prompt},
+    ]
+
+    output = generator(
+        messages,
+        max_new_tokens=220,
+        do_sample=True,
+        temperature=0.7,
+        pad_token_id=generator.tokenizer.eos_token_id,
+    )
+
+    # Extract the assistant's reply from the chat template output
+    generated = output[0]["generated_text"]
+    if isinstance(generated, list):
+        # Chat format: last message is the assistant reply
+        reply = generated[-1]["content"]
+    else:
+        # Fallback: strip the prompt
+        reply = generated[len(prompt):]
+
+    return reply.strip()
+
+
+def process_entry(audio_path):
+    """Pipeline: audio → transcript → reflection."""
+    if audio_path is None:
+        return (
+            "",
+            "Please record something first, then click Reflect.",
+        )
+
+    transcript = transcribe(audio_path)
+    if not transcript:
+        return (
+            "",
+            "Couldn't make out any speech. Try speaking a bit louder or closer to the mic.",
+        )
+
+    reflection = reflect(transcript)
+    return transcript, reflection
+
+
+
+DESCRIPTION = """
+## Voice Journal
+*Speak your day. Get it reflected back.*
+
+Record a voice note about anything — how your day went, what's on your mind, 
+something that happened. A small AI model will listen and gently reflect it back to you.
+
+Everything runs locally. Nothing leaves your machine.
+"""
+
+FOOTER = """
+---
+*Built for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon) · 
+Whisper (140M) + Qwen2.5-7B · No cloud APIs · Runs on your laptop*
+"""
+
+custom_css = """
+body { font-family: 'Georgia', serif; }
+
+#title-block { text-align: center; padding: 1.5rem 0 0.5rem; }
+
+#record-col { display: flex; flex-direction: column; gap: 0.75rem; }
+
+#reflect-btn {
+    background: #2d4a3e !important;
+    color: #f0ebe0 !important;
+    border: none !important;
+    border-radius: 8px !important;
+    font-size: 1rem !important;
+    padding: 0.75rem !important;
+    cursor: pointer;
+}
+
+#reflect-btn:hover { background: #1f3429 !important; }
+
+#transcript-box textarea, #reflection-box textarea {
+    font-family: 'Georgia', serif !important;
+    font-size: 0.95rem !important;
+    line-height: 1.65 !important;
+    background: #faf8f3 !important;
+    border: 1px solid #d8d0c0 !important;
+    border-radius: 8px !important;
+}
+
+#reflection-box textarea {
+    background: #f0ede4 !important;
+    color: #2a2a2a !important;
+}
+
+footer { display: none !important; }
+"""
+
+with gr.Blocks(
+    title="Voice Journal",
+    theme=gr.themes.Soft(
+        primary_hue="emerald",
+        neutral_hue="stone",
+        font=gr.themes.GoogleFont("Lora"),
+    ),
+    css=custom_css,
+) as app:
+
+    with gr.Column(elem_id="title-block"):
+        gr.Markdown(DESCRIPTION)
+
+    with gr.Row(equal_height=False):
+        # Left column — input
+        with gr.Column(scale=1, elem_id="record-col"):
+            audio_input = gr.Audio(
+                sources=["microphone"],
+                type="filepath",
+                label="Record your entry",
+                show_download_button=False,
+            )
+            reflect_btn = gr.Button("Reflect →", elem_id="reflect-btn", variant="primary")
+
+        # Right column — output
+        with gr.Column(scale=1):
+            transcript_box = gr.Textbox(
+                label="What you said",
+                lines=5,
+                interactive=False,
+                placeholder="Your words will appear here after you click Reflect…",
+                elem_id="transcript-box",
+            )
+            reflection_box = gr.Textbox(
+                label="Reflection",
+                lines=8,
+                interactive=False,
+                placeholder="Your reflection will appear here…",
+                elem_id="reflection-box",
+            )
+
+    reflect_btn.click(
+        fn=process_entry,
+        inputs=audio_input,
+        outputs=[transcript_box, reflection_box],
+        api_name="reflect",
+    )
+
+    gr.Markdown(FOOTER)
+
+if __name__ == "__main__":
+    app.launch(share=False)