Create app.py

app.py

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+from ctransformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+import torch
+import gradio as gr
+import os
+import time
+model = AutoModelForCausalLM.from_pretrained("alibidaran/llama-2-7b-virtual_doctor-gguf",hf=True)
+tokenizer = AutoTokenizer.from_pretrained("alibidaran/llama-2-7b-virtual_doctor")
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+
+def add_text(history, text):
+    history = history + [(text,None)]
+    return history, gr.Textbox(value="", interactive=False)
+
+
+def add_file(history, file):
+    global image_file
+    image_file=file.name
+    history = history + [((file.name,),None)]
+    return history
+
+
+def bot(history):
+    prompt=history[-1][0]
+    text=f"<s> ###Human: {prompt} ###Asistant: "
+    inputs=tokenizer(text,return_tensors='pt').to('cpu')
+    with torch.no_grad():
+      outputs=model.generate(**inputs,max_new_tokens=200,do_sample=True,top_p=0.92,top_k=10,temperature=0.7)
+    response=tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print(response)
+    history[-1][1] = ""
+    for character in response[1:-1]:
+        history[-1][1] += character
+        time.sleep(0.01)
+        yield history
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(
+        [],
+        elem_id="chatbot",
+        bubble_full_width=False,
+        #avatar_images=(None, (os.path.join(os.path.dirname(__file__), "avatar.png"))),
+    )
+
+    with gr.Row():
+        txt = gr.Textbox(
+            scale=4,
+            show_label=False,
+            placeholder="Enter text and press enter, or upload an image",
+            container=False,
+        )
+        btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
+
+    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+        bot, chatbot, chatbot, api_name="bot_response"
+    )
+    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+
+    chatbot.like(print_like_dislike, None, None)
+
+
+
+if __name__=="__main__":
+  demo.launch(share=True,debug=True)