Update app.py

app.py

@@ -1,70 +1,121 @@
+# app.py
+# Simple Gradio app to run inference with Flan-T5 models (text2text-generation)
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline
+import torch
+import os
+
+# Cache pipelines for models so we don't reload on every request
+PIPES = {}
+
+DEFAULT_MODELS = {
+    "flan-t5-small": "google/flan-t5-small",
+    "flan-t5-base": "google/flan-t5-base",
+    "flan-t5-large": "google/flan-t5-large",
+    # you can add "flan-t5-xl" or others if your Space has enough RAM/GPU
+}
 
+def get_device():
+    return 0 if torch.cuda.is_available() else -1
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+def get_pipeline(model_key_or_name):
     """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    Returns a transformers pipeline for the given model.
+    model_key_or_name: either a key from DEFAULT_MODELS or a full model name.
     """
-    client = InferenceClient(token=hf_token.token, model="devagonal/flan-t5-rouge-squad-qg-test-01")
+    model_name = DEFAULT_MODELS.get(model_key_or_name, model_key_or_name)
+    if model_name in PIPES:
+        return PIPES[model_name]
+    device = get_device()
+    # pipeline will handle tokenizer/model download
+    pipe = pipeline(
+        "text2text-generation",
+        model=model_name,
+        tokenizer=model_name,
+        device=device,
+        # trust_remote_code=False by default; for official Flan-T5 models this is fine
+    )
+    PIPES[model_name] = pipe
+    return pipe
 
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
+def generate(prompt: str, model_choice: str, max_length: int, temperature: float, num_return_sequences: int):
+    """
+    Generate text from the prompt using the selected Flan-T5 model.
+    """
+    if not prompt or not prompt.strip():
+        return "Please enter a prompt."
+    try:
+        pipe = get_pipeline(model_choice)
+    except Exception as e:
+        return f"Failed to load model {model_choice}: {e}"
 
-    messages.append({"role": "user", "content": message})
+    # transformers pipeline arguments:
+    do_sample = temperature > 0.0
+    try:
+        outputs = pipe(
+            prompt,
+            max_length=max_length,
+            do_sample=do_sample,
+            temperature=float(temperature),
+            num_return_sequences=int(num_return_sequences),
+            # return_full_text=False is default for text2text-generation
+        )
+    except Exception as e:
+        return f"Generation failed: {e}"
 
-    response = ""
+    # outputs is a list of dicts with key 'generated_text'
+    texts = [o.get("generated_text", "") for o in outputs]
+    # Join multiple outputs with separators
+    return "\n\n---\n\n".join(texts)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
 
-        response += token
-        yield response
+with gr.Blocks(title="Flan-T5 Inference (Text2Text)") as demo:
+    gr.Markdown(
+        """
+        # Flan-T5 Text2Text Inference
+        Type your prompt and pick a Flan-T5 model. For best performance, enable a GPU in the Space settings.
+        """
+    )
 
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(
+                lines=8,
+                label="Input prompt",
+                placeholder="e.g. Summarize the following article in 2 sentences: ..."
+            )
+            examples = gr.Examples(
+                examples=[
+                    ["Summarize the key points of the American Declaration of Independence."],
+                    ["Translate the following English sentence to French: 'The weather is nice today.'"],
+                    ["Explain in simple terms how photosynthesis works."],
+                ],
+                inputs=prompt
+            )
+        with gr.Column(scale=1):
+            model_choice = gr.Dropdown(list(DEFAULT_MODELS.keys()), value="flan-t5-base", label="Model")
+            max_length = gr.Slider(32, 1024, value=256, step=1, label="Max length (tokens)")
+            temperature = gr.Slider(0.0, 1.5, value=0.0, step=0.01, label="Temperature (0.0 = deterministic)")
+            num_return_sequences = gr.Slider(1, 3, value=1, step=1, label="Number of outputs")
+            run_btn = gr.Button("Generate")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    output = gr.Textbox(label="Model output", lines=12)
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    run_btn.click(
+        fn=generate,
+        inputs=[prompt, model_choice, max_length, temperature, num_return_sequences],
+        outputs=output,
+    )
 
+    gr.Markdown(
+        """
+        Notes:
+        - If you want faster generation, set Space to use a GPU (in Settings → Hardware).
+        - Larger models (flan-t5-large / flan-t5-xl) need more RAM — they may OOM on CPU or free GPU tiers.
+        - You can add other models to DEFAULT_MODELS above or input a full model name from the Hub.
+        """
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()