Update app.py

app.py

@@ -1,14 +1,42 @@
-from transformers import AutoTokenizer, GemmaForSequenceClassification
+import os
+import json
 import gradio as gr
-import os 
-model = GemmaForSequenceClassification.from_pretrained("google/gemma-2b")
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
-def greet(name):
-  x = tokenizer(name,return_tensors='pt')
-  with torch.no_grad():
-      preds = model(**inputs)
-  
-  return preds
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+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,
+}
+
+
+model = genai.GenerativeModel(
+    model_name="gemini-pro",
+    generation_config=generation_config,
+)
+
+task_description = " You need to classify each message you receive among the following categories: 'admiration','amusement','anger','annoyance','approval','caring','confusion','curiosity','desire','disappointment','disapproval','disgust','embarrassment','excitement','fear','gratitude','grief','joy','love','nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'<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(message):
+    prompt_parts = [
+        task_description,
+        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)
+
+iface = gr.Interface(fn=classify_msg, inputs="text", outputs="text")
 iface.launch()