Create app.py

app.py

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+import os
+import gradio as gr
+import torch
+from transformers import pipeline
+
+# ─── 1) SET UP PIPELINES ──────────────────────────────────────────────────────
+# Sentiment analysis (local)
+sentiment_pipe = pipeline("sentiment-analysis")
+
+# Text generation chat (local GPT-2)
+device = 0 if torch.cuda.is_available() else -1
+chat_pipe = pipeline(
+    "text-generation",
+    model="distilgpt2",
+    tokenizer="distilgpt2",
+    device=device,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7
+)
+
+def respond(message, chat_history):
+    """
+    - If the user starts with "Sentiment:", run sentiment analysis on the rest.
+    - Otherwise fall back to GPT-2 chat continuation.
+    """
+    if message.lower().startswith("sentiment:"):
+        text = message[len("sentiment:"):].strip()
+        result = sentiment_pipe(text)[0]
+        label = result["label"]
+        score = result["score"]
+        reply = f"🔍 Sentiment: **{label}** (score: {score:.3f})"
+    else:
+        # GPT-2 continuation
+        out = chat_pipe(message)
+        # the pipeline returns [{'generated_text': "..."}]
+        reply = out[0]["generated_text"].strip()
+    chat_history.append(("You", message))
+    chat_history.append(("Bot", reply))
+    return chat_history
+
+# ─── 2) BUILD THE UI ───────────────────────────────────────────────────────────
+with gr.Blocks() as demo:
+    gr.Markdown("## 😊 Sentiment-&-Chat Bot\n"
+                "_Type `Sentiment: <your text>` to analyze sentiment, or just chat!_")
+    chat = gr.Chatbot()
+    msg  = gr.Textbox(placeholder="Type here…", show_label=False)
+
+    msg.submit(respond, [msg, chat], [chat])
+
+# queue() creates the `/api/predict` endpoint Spaces needs
+demo = demo.queue()
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    demo.launch(server_name="0.0.0.0", server_port=port)