Create app.py

app.py

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+import os
+import json
+import gradio as gr
+import google.generativeai as genai
+
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Set up the model
+generation_config = {
+    "temperature": 0.9,
+    "top_p": 1,
+    "top_k": 1,
+    "max_output_tokens": 2048,
+}
+
+safety_settings = [
+    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {
+        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE",
+    },
+    {
+        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE",
+    },
+]
+
+model = genai.GenerativeModel(
+    model_name="gemini-pro",
+    generation_config=generation_config,
+    safety_settings=safety_settings,
+)
+
+task_description = " You are an SMS (Short Message Service) reader who reads every message that the short message service centre receives and you need to classify each message among the following categories: {}<div>Let the output be a softmax function output giving the probability of message belonging to each category.</div><div>The sum of the probabilities should be 1</div><div>The output must be in JSON format</div>"
+
+
+def classify_msg(categories, message):
+    prompt_parts = [
+        task_description.format(categories),
+        f"Message: {message}",
+        "Category: ",
+    ]
+
+    response = model.generate_content(prompt_parts)
+
+    json_response = json.loads(
+        response.text[response.text.find("{") : response.text.rfind("}") + 1]
+    )
+
+    return gr.Label(json_response)
+
+
+def clear_inputs_and_outputs():
+    return [None, None, None]
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        <h1 align="center">Multi-language Text Classifier using Gemini Pro</h1> \
+        This space uses Gemini Pro in order to classify texts.<br> \
+        Depending on the list of categories that you specify, you can have text classifier, a SPAM detector, a sentiment classifier, ... <br><br> \
+        <b>For the categories, enter a list of words separated by commas</b><br><br>
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                categories = gr.Textbox(
+                    label="Categories",
+                    placeholder="Input the list of categories as comma separated words",
+                )
+            with gr.Row():
+                message = gr.Textbox(label="Message", placeholder="Enter Message")
+            with gr.Row():
+                clr_btn = gr.Button(value="Clear", variant="secondary")
+                csf_btn = gr.Button(value="Classify")
+        with gr.Column():
+            lbl_output = gr.Label(label="Prediction")
+
+    clr_btn.click(
+        fn=clear_inputs_and_outputs,
+        inputs=[],
+        outputs=[categories, message, lbl_output],
+    )
+    csf_btn.click(
+        fn=classify_msg,
+        inputs=[categories, message],
+        outputs=[lbl_output],
+    )
+
+    gr.Examples(
+        examples=[
+            ["Normal, Promotional, Urgent", "Will you be passing by?"],
+            ["Spam, Ham", "Plus de 300 % de perte de poids pendant le régime."],
+            ["Χαρούμενος, Δυστυχισμένος", "Η εξυπηρέτηση σας ήταν απαίσια"],
+            ["مهم، أقل أهمية ", "خبر عاجل"],
+        ],
+        inputs=[categories, message],
+        outputs=lbl_output,
+        fn=classify_msg,
+        cache_examples=True,
+    )
+
+demo.queue(api_open=False)
+demo.launch(debug=True, share=True, show_api=False)