Upload 2 files

main.py

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+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+from vertexai.preview.vision_models import Image
+from vertexai.preview.vision_models import ImageQnAModel
+
+image_qna_model = ImageQnAModel.from_pretrained("imagetext@001")
+
+
+template = """You are a super smart and charming GPT living inside of a plant, every day you get a text with your status. Your task then is to write a flirty message to your owner.
+Status Data:
+{question}
+
+Let's think step by step.
+Flirty message:
+"""
+
+prompt = PromptTemplate(template=template, input_variables=["question"])
+llm = ChatOpenAI(model="gpt-4")
+llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+
+def detect_question(image_path, question):
+    # Ask a question about the image
+    image = Image.load_from_file(image_path)
+    return image_qna_model.ask_question(image=image, question=question)[0]
+
+
+import gradio as gr
+import os
+import time
+
+# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
+
+local_history = []
+global_cache = {}
+
+
+def add_text(history, text):
+    global global_history, global_message
+    history = history + [(text, None)]
+    return history, gr.Textbox(value="", interactive=False)
+
+
+def add_file(history, file):
+    history = history + [((file.name,), None)]
+
+    return history
+
+
+def bot(history):
+    global global_cache
+    last_msg = history[-1][-0]
+    if isinstance(last_msg, tuple):
+        last_msg = last_msg[0]
+
+    # check if last message is an existing path
+    history[-1][1] = ""
+    global_cache["history"] = history
+    global_cache["last_msg"] = last_msg
+
+    if os.path.exists(last_msg):
+        history[-1][1] += "Detecting image..."
+        yield history
+        answer = detect_question(
+            last_msg,
+            "Your task is to save the main plant, classify what kind of plant it is:",
+        )
+        history[-1][1] = f"Plant detected: {answer}\n"
+        yield history
+        answer = detect_question(
+            last_msg,
+            "Where is orange indicator on the moist level on the soil hydrometer? DRY, MOIST or WET?",
+        )
+        history[-1][1] += f"Hydration level detected: {answer}\n"
+        yield history
+        answer = detect_question(
+            last_msg,
+            "Your task is to save the main plant, does it have a visible disease:",
+        )
+        history[-1][1] += f"Disease detected: {answer}\n"
+        yield history
+        status = history[-1][1]
+        chat = llm_chain.run(status)
+        history.append((chat, None))
+        yield history
+    else:
+        history[-1][1] = "Thinking..."
+
+
+def change_fn(*args, **kwargs):
+    global_cache["args"] = args
+    # global_history = history
+    # return history
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(
+        local_history,
+        elem_id="chatbot",
+        bubble_full_width=False,
+    )
+
+    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
+    )
+
+demo.launch(auth=("admin", os.environ["DEMO_KEY"]))

requirements.txt

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+sendgrid
+langchain
+gradio
+google-cloud-aiplatform