Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from transformers import BlenderbotSmallTokenizer, BlenderbotSmallForConditionalGeneration
+
+# --- Load model and tokenizer ---
+model_name = "facebook/blenderbot-90M"
+tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_name)
+model = BlenderbotSmallForConditionalGeneration.from_pretrained(model_name)
+
+# --- Define chatbot logic ---
+def chat_with_bot(message, history):
+    if not message:
+        return "Hi there! 👋 Ask me something to get started."
+
+    # Rebuild conversation context
+    conversation = ""
+    if history:
+        for turn in history:
+            role = turn.get("role")
+            content = turn.get("content")
+            if role == "user":
+                conversation += f"User: {content}\n"
+            elif role == "assistant":
+                conversation += f"Bot: {content}\n"
+
+    conversation += f"User: {message}\nBot:"
+
+    # Tokenize input and generate reply
+    inputs = tokenizer(
+        conversation,
+        return_tensors="pt",
+        truncation=True,
+        padding="max_length",
+        max_length=512,
+    )
+    reply_ids = model.generate(**inputs, max_length=120)
+    reply = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
+    return reply
+
+# --- Define welcome message ---
+initial_message = [
+    {"role": "assistant", "content": "👋 Hello! I’m your chatbot. Ask me anything to start our conversation!"}
+]
+
+# --- Create Gradio Interface ---
+demo = gr.ChatInterface(
+    fn=chat_with_bot,
+    title="🤖 Mini Chatbot (Facebook BlenderBot-90M)",
+    description="Hi 👋 I’m a small conversational chatbot powered by Facebook’s BlenderBot-90M.",
+    theme="soft",
+    type="messages",
+    examples=["Hello!", "Tell me a fun fact", "How are you today?"],
+)
+
+# --- Launch App ---
+if __name__ == "__main__":
+    demo.launch(share=True)