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app.py

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+import gradio as gr
+import torch
+from torchvision.models import resnet50, ResNet50_Weights
+from PIL import Image
+import tempfile
+from gtts import gTTS
+import whisper
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# ----- 画像認識用モデル (ResNet-50) -----
+weights = ResNet50_Weights.IMAGENET1K_V2
+img_model = resnet50(weights=weights)
+img_model.eval()
+img_transform = weights.transforms()
+imagenet_classes = weights.meta["categories"]
+
+
+def image_classify(img: Image.Image):
+    img_tensor = img_transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = img_model(img_tensor)
+    probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    result = {imagenet_classes[top5_catid[i]]: float(top5_prob[i]) for i in range(5)}
+    return result
+
+
+model_name = "cyberagent/open-calm-1b"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", torch_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name, use_fast=True, trust_remote_code=True
+)
+
+text_gen_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=128,
+    temperature=0.7,
+    top_p=0.9,
+    pad_token_id=tokenizer.eos_token_id,
+)
+
+
+# ----- 言語モデル (LM) -----
+def generate_text(prompt):
+    # promptに基づき続きのテキストを生成
+    result = text_gen_pipeline(prompt, do_sample=True, num_return_sequences=1)
+    generated_text = result[0]["generated_text"]
+    # prompt部分を含めた全文が返るので、prompt部分はそのままでOK
+    return generated_text
+
+
+# ----- 音声合成 (TTS) -----
+def text_to_speech(text, lang="ja"):
+    tts = gTTS(text=text, lang=lang)
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
+        tts.save(fp.name)
+        return fp.name
+
+
+# ----- 音声認識 (ASR) -----
+whisper_model = whisper.load_model("small")
+
+
+def speech_to_text(audio_file):
+    result = whisper_model.transcribe(audio_file)
+    return result["text"]
+
+
+# ----- Gradio UI -----
+def run():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 画像認識・言語モデル・音声合成・音声認識")
+
+        with gr.Tabs():
+            with gr.TabItem("画像認識"):
+                gr.Markdown("### 画像認識 (ResNet-50)")
+                gr.Interface(
+                    fn=image_classify,
+                    inputs=gr.Image(type="pil"),
+                    outputs=gr.Label(num_top_classes=5),
+                    description="画像をアップロードして分類します。(ImageNet)",
+                )
+
+            with gr.TabItem("言語モデル"):
+                gr.Markdown("### 言語モデル")
+                lm_output = gr.Textbox(label="生成結果")
+                user_input = gr.Textbox(label="入力テキスト")
+                send_btn = gr.Button("送信")
+                send_btn.click(generate_text, inputs=user_input, outputs=lm_output)
+
+            with gr.TabItem("音声合成"):
+                gr.Markdown("### 音声合成 (gTTS)")
+                tts_input = gr.Textbox(label="音声にしたいテキスト")
+                tts_output = gr.Audio(label="合成音声")
+                tts_button = gr.Button("合成")
+                tts_button.click(text_to_speech, inputs=tts_input, outputs=tts_output)
+
+            with gr.TabItem("音声認識"):
+                gr.Markdown("### 音声認識 (Whisper)")
+                gr.Interface(
+                    fn=speech_to_text,
+                    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
+                    outputs="text",
+                    description="マイクから録音して文字起こし",
+                )
+
+    demo.launch()
+
+if __name__ == "__main__":
+    run()

requirements.txt

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+torch
+torchvision
+transformers
+accelerate
+gTTS
+git+https://github.com/openai/whisper.git
+ffmpeg-python
+gradio