Create main.py

main.py

@@ -0,0 +1,50 @@
+from fastapi import FastAPI
+import os
+import json
+import google.generativeai as genai
+
+app = FastAPI()
+
+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>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['category'])
+
+    
+@app.get("/")
+async def root():
+ return {"Text Emotion Classification":"Version 1.5 'Text'"}
+
+@app.post("/classify/")
+def read_user(text: str):
+    return classify_msg(text)