Create app.py

app.py

@@ -0,0 +1,94 @@
+import gradio as gr
+import requests
+import json
+from transformers import (
+    GPT2Tokenizer,
+    GPT2LMHeadModel,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+)
+import torch
+
+model_specs = [
+    {
+        "name": "distilgpt2",
+        "tokenizer": GPT2Tokenizer.from_pretrained("distilgpt2"),
+        "model": GPT2LMHeadModel.from_pretrained("distilgpt2"),
+    },
+    {
+        "name": "openai-gpt",
+        "tokenizer": AutoTokenizer.from_pretrained("openai-gpt"),
+        "model": AutoModelForCausalLM.from_pretrained("openai-gpt"),
+    },
+    {
+        "name": "OpenChatKit",
+        "tokenizer": AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B"),
+        "model": AutoModelForCausalLM.from_pretrained(
+            "togethercomputer/GPT-NeoXT-Chat-Base-20B", torch_dtype=torch.float16
+        ).to("cuda:0"),
+    },
+    {
+        "name": "flan-t5-xl",
+        "tokenizer": AutoTokenizer.from_pretrained("google/flan-t5-xl"),
+        "model": AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-xl"),
+    },
+    {
+        "name": "LLama",
+        "tokenizer": AutoTokenizer.from_pretrained("decapoda-research/llama-7b-hf"),
+        "model": AutoModelForCausalLM.from_pretrained("decapoda-research/llama-7b-hf"),
+    },
+]
+
+def generate_response(model_name, input_text, speak_output):
+    selected_model = next(
+        (spec for spec in model_specs if spec["name"] == model_name), None
+    )
+    if not selected_model:
+        return "Invalid model selected."
+
+    tokenizer = selected_model["tokenizer"]
+    model = selected_model["model"]
+
+    if model_name in ["flan-t5-xl"]:
+        # For Seq2Seq models
+        input_ids = tokenizer.encode(input_text, return_tensors="pt")
+        output_ids = model.generate(input_ids)
+        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    else:
+        # For CausalLM models
+        input_ids = tokenizer.encode(input_text, return_tensors="pt")
+        output_ids = model.generate(input_ids, max_length=input_ids.shape[1] + 50)
+        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    if speak_output:
+        ELEVENLABS_API_URL = "https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM"
+        HEADERS = {
+            "accept": "audio/mpeg",
+            "xi-api-key": "9360e4509988dc28ff03a5d43cb6941b",
+            "Content-Type": "application/json",
+        }
+        data = {
+            "text": response,
+            "voice_settings": {"stability": 0, "similarity_boost": 0},
+        }
+        response_audio = requests.post(ELEVENLABS_API_URL, headers=HEADERS, data=json.dumps(data))
+
+        with open("response_audio.mp3", "wb") as f:
+            f.write(response_audio.content)
+
+    return response
+
+if __name__ == "__main__":
+    model_names = [spec["name"] for spec in model_specs]
+    model_dropdown = gr.inputs.Dropdown(model_names, label="Select Model")
+    text_input = gr.inputs.Textbox(lines=5, label="Input Text")
+    speak_output = gr.inputs.Checkbox(label="Speak output with ElevenLabs API")
+
+    gr.Interface(
+        generate_response,
+        inputs=[model_dropdown, text_input, speak_output],
+        outputs="text",
+        title="Chat Model Selection",
+        description="Select a chat model and enter text to generate a response.",
+    ).launch()