Rename spp to app.py

app.py

@@ -0,0 +1,68 @@
+import requests
+import os
+from transformers import pipeline
+
+
+from transformers import Tool
+# Import other necessary libraries if needed
+
+class TextGenerationTool(Tool):
+    name = "text_generator"
+    description = (
+        "This is a tool for text generation. It takes a prompt as input and returns the generated text."
+    )
+
+    inputs = ["text"]
+    outputs = ["text"]
+
+    def __call__(self, prompt: str):
+        #API_URL = "https://api-inference.huggingface.co/models/openchat/openchat_3.5"
+        #headers = {"Authorization": "Bearer " +  os.environ['hf']}
+        token=os.environ['HF_token']
+        #payload = {
+        #    "inputs": prompt  # Adjust this based on your model's input format
+        #}
+
+        #payload = {
+        #            "inputs": "Can you please let us know more details about your ",
+        #        }
+        
+        #def query(payload):
+        #generated_text = requests.post(API_URL, headers=headers, json=payload).json()
+        #print(generated_text)
+        #return generated_text["text"]
+            
+        # Replace the following line with your text generation logic
+        #generated_text = f"Generated text based on the prompt: '{prompt}'"
+
+        # Initialize the text generation pipeline
+        #text_generator = pipeline(model="lgaalves/gpt2-dolly", token=token)
+        text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)
+
+        # Generate text based on a prompt
+        generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
+
+        # Print the generated text
+        print(generated_text)
+
+
+
+        return generated_text
+        
+        # Define the payload for the request
+        #payload = {
+        #    "inputs": prompt  # Adjust this based on your model's input format
+        #}
+
+        # Make the request to the API
+        #generated_text = requests.post(API_URL, headers=headers, json=payload).json()
+
+        # Extract and return the generated text
+        #return generated_text["generated_text"]
+
+# Uncomment and customize the following lines based on your text generation needs
+# text_generator = pipeline(model="gpt2")
+# generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
+
+# Print the generated text if needed
+# print(generated_text)