Create app.py

app.py

@@ -0,0 +1,50 @@
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+from nltk.tokenize import sent_tokenize
+import torch
+
+model = "janny127/autotrain-5e45b-p5z66"
+tokenizer = AutoTokenizer.from_pretrained(model)
+
+pipeline = pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+def predict(prompt, history):
+    # Prompt
+    formatted_prompt = (
+        f"### Human: {prompt}### Assistant:"
+    )
+    
+    # Generate the Texts
+    sequences = pipeline(
+        formatted_prompt,
+        do_sample=True,
+        top_k=50,
+        top_p = 0.7,
+        num_return_sequences=1,
+        repetition_penalty=1.1,
+        max_new_tokens=500,
+    )
+    generated_text = sequences[0]['generated_text']
+
+    final_result = generated_text.split("### Assistant:")[1]
+    if " Human: " in final_result:
+        final_result = final_result.split(" Human: ")[0]
+    if " #" in final_result:
+        final_result = final_result.split(" #")[0]
+    
+    # return generated_text.strip()
+    return final_result.strip()
+
+gr.ChatInterface(predict,
+                 title="Tinyllama_chatBot",
+                 description="Ask Tiny llama any questions",
+                 examples=['How to cook a fish?', 'Who is the president of US now?']
+                 ).launch()  # Launching the web interface.
+
+