Update app.py

app.py

@@ -1,64 +1,146 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
+import os
+import re
+from datetime import datetime
 
+import gradio as gr
+import openai
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Set OpenAI API key from an environment variable.
+openai.api_key = os.environ["OPENAI_API_KEY"]
 
-    messages.append({"role": "user", "content": message})
+def get_evaluation_questions():
+    """
+    Loads evaluation questions and expected answers from environment variables.
+    Expected environment variable names are:
+      TEST_QUESTION_1, TEST_EXPECTED_1,
+      TEST_QUESTION_2, TEST_EXPECTED_2, and so on.
+    """
+    questions = []
+    i = 1
+    while True:
+        question = os.environ.get(f"TEST_QUESTION_{i}")
+        expected = os.environ.get(f"TEST_EXPECTED_{i}")
+        if not question or not expected:
+            break
+        questions.append({"question": question, "expected": expected})
+        i += 1
+    return questions
 
-    response = ""
+# Load the evaluation questions once at startup.
+EVALUATION_QUESTIONS = get_evaluation_questions()
 
-    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
+def init_sheet():
+    """
+    Initializes and returns the Google Sheet.
+    The sheet name is taken from the SHEET_NAME environment variable,
+    defaulting to "Prompt Evaluations" if not set.
+    
+    Ensure that your service account credentials (credentials.json)
+    are available and that the sheet is shared with the service account's email.
+    """
+    scopes = [
+        "https://spreadsheets.google.com/feeds",
+        "https://www.googleapis.com/auth/spreadsheets",
+        "https://www.googleapis.com/auth/drive"
+    ]
+    creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scopes)
+    client = gspread.authorize(creds)
+    sheet_name = os.environ.get("SHEET_NAME", "Prompt Evaluations")
+    sheet = client.open(sheet_name).sheet1
+    return sheet
 
-        response += token
-        yield response
+def sanitize_input(text):
+    """
+    Sanitizes input to prevent harmful content and limits its length.
+    """
+    # Allow alphanumerics and some punctuation, then truncate to 500 characters.
+    clean_text = re.sub(r"[^a-zA-Z0-9\s.,!?@:\-]", "", text)
+    return clean_text.strip()[:500]
 
+def evaluate_prompt(email, name, system_prompt):
+    """
+    For each test question:
+      - Uses the provided system prompt to generate a response with GPT-4o Mini.
+      - Checks if the expected substring is present.
+      - Computes an aggregate score.
+    Logs the user's email, name, system prompt, and score to a Google Sheet.
+    """
+    # Sanitize the inputs.
+    email = sanitize_input(email)
+    name = sanitize_input(name)
+    system_prompt = sanitize_input(system_prompt)
 
-"""
-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)",
-        ),
-    ],
-)
+    score = 0
+    responses = []
+    for item in EVALUATION_QUESTIONS:
+        question = item["question"]
+        expected = item["expected"]
+        try:
+            response = openai.ChatCompletion.create(
+                model="gpt-4o-mini",  # Ensure this identifier matches the deployed model.
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": question}
+                ]
+            )
+            answer = response.choices[0].message["content"].strip()
+        except Exception as e:
+            answer = f"Error during OpenAI API call: {str(e)}"
+        
+        # Simple evaluation: check if the answer contains the expected substring.
+        if expected.lower() in answer.lower():
+            score += 1
+            verdict = "Correct"
+        else:
+            verdict = "Incorrect"
+        
+        responses.append(
+            f"Question: {question}\n"
+            f"Answer: {answer}\n"
+            f"Expected: {expected}\n"
+            f"Result: {verdict}\n"
+        )
+    
+    result_details = "\n".join(responses)
+    
+    try:
+        sheet = init_sheet()
+        timestamp = datetime.now().isoformat()
+        row = [timestamp, email, name, score, system_prompt]
+        sheet.append_row(row)
+    except Exception as err:
+        print("Error writing to Google Sheet:", err)
+    
+    return f"Your evaluation score is {score} out of {len(EVALUATION_QUESTIONS)}.\n\nDetails:\n{result_details}"
 
+def build_interface():
+    """
+    Constructs the Gradio interface.
+    """
+    with gr.Blocks() as demo:
+        gr.Markdown("# GPT-4o Mini Prompt Evaluation")
+        gr.Markdown("Enter your email, name, and a system prompt below:")
+        
+        email_input = gr.Textbox(label="Email", placeholder="your.email@example.com")
+        name_input = gr.Textbox(label="Name", placeholder="Your name")
+        system_prompt_input = gr.Textbox(
+            label="System Prompt",
+            placeholder="Enter your system prompt here...",
+            lines=6
+        )
+        eval_button = gr.Button("Evaluate")
+        output_text = gr.Textbox(label="Results", lines=15)
+        
+        eval_button.click(
+            fn=evaluate_prompt,
+            inputs=[email_input, name_input, system_prompt_input],
+            outputs=output_text
+        )
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
+    interface = build_interface()
+    # Launch the app on 0.0.0.0 so it is accessible externally (e.g., in a container).
+    interface.launch(server_name="0.0.0.0", server_port=7860)