initial

app.py

@@ -1,64 +1,64 @@
+import os
+from pydantic import BaseModel, Field, validator, ValidationError
 import gradio as gr
-from huggingface_hub import InferenceClient
+from openai import OpenAI
+from typing import List, Dict, Any, Optional, Literal, Union
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Chatbot model 
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+client = OpenAI()
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+class FlamingConvention(BaseModel):
+    pass
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    
+def generate_json(specification): 
+    """
+    Function to prompt OpenAI API to generate structured JSON output.
+    """
 
-    messages.append({"role": "user", "content": message})
+    try:
+        # Call OpenAI API to generate structured output based on prompt
+        response = client.beta.chat.completions.parse(
+            model="gpt-4o-2024-08-06",  # Use GPT model that supports structured output
+            messages=[
+                {"role": "system", "content": "Extract the farm management information."},
+                {"role": "user", "content": specification}
+            ],
+            response_format=FlamingConvention,
+        )
+        
+        generated_json = response.choices[0].message.parsed
+        print(generated_json) # debugging
 
-    response = ""
+        pretty_json = generated_json.json()
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        if 'error' in response:
+            raise ValueError(f"API error: {response['error']['message']}")
+            
+        return pretty_json
 
-        response += token
-        yield response
+    except ValidationError as e:
+        return {"error": str(e)}
+    except Exception as e:
+        return {"error": "Failed to generate valid JSON. " + str(e)}
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    
+def process_specifications(data):
+    # This method just drives the process
+    resulting_schema = generate_json(data)
+    return resulting_schema 
+    
+    
+demo = gr.Interface(
+    fn=process_specifications, 
+    inputs=[gr.Textbox(label="Necessary Values: Plant Asset ID, Plant asset name, Plant Notes, Activity Name, Activity ID, Activity Timestamp, Activity Notes")],
+            outputs=[gr.Textbox(label="JSON Data Output")],
+            title="JSON Schema Crafting Experiment",
+            description="Input your specification, receive the schema and payload!",
+            allow_flagging="never")
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()