Update autotemp.py

autotemp.py

@@ -4,6 +4,7 @@ import os
 import re
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import gradio as gr
+import traceback
 
 # Load environment variables from .env file
 load_dotenv()
@@ -24,7 +25,7 @@ class AutoTemp:
     def generate_with_openai(self, prompt, temperature, top_p, retries=3):
         while retries > 0:
             try:
-                response = openai.ChatCompletion.create(
+                response = openai.chat.completions.create(
                     model=self.model_version,
                     messages=[
                         {"role": "system", "content": "You are a helpful assistant."},
@@ -33,13 +34,17 @@ class AutoTemp:
                     temperature=temperature,
                     top_p=top_p
                 )
-                message = response['choices'][0]['message']['content']
+                # Adjusted to use attribute access instead of dictionary access
+                message = response.choices[0].message.content
                 return message.strip()
             except Exception as e:
                 retries -= 1
+                print(f"Attempt failed with error: {e}")  # Print the error for debugging
                 if retries <= 0:
+                    print(f"Final error generating text at temperature {temperature} and top-p {top_p}: {e}")
                     return f"Error generating text at temperature {temperature} and top-p {top_p}: {e}"
 
+
     def evaluate_output(self, output, temperature, top_p):
         fixed_top_p_for_evaluation = 1.0
         eval_prompt = f"""
@@ -75,10 +80,14 @@ class AutoTemp:
             }
             for future in as_completed(future_to_temp):
                 temp = future_to_temp[future]
-                output_text = future.result()
-                if output_text and not output_text.startswith("Error"):
-                    outputs[temp] = output_text
-                    scores[temp] = self.evaluate_output(output_text, temp, top_p)  # Pass top_p here
+                try:
+                    output_text = future.result()
+                    print(f"Output for temp {temp}: {output_text}")  # Print the output for debugging
+                    if output_text and not output_text.startswith("Error"):
+                        outputs[temp] = output_text
+                        scores[temp] = self.evaluate_output(output_text, temp, top_p)  # Pass top_p here
+                except Exception as e:
+                    print(f"Error while generating or evaluating output for temp {temp}: {e}")
 
         if not scores:
             return "No valid outputs generated.", None
@@ -107,7 +116,7 @@ def main():
         inputs=[
             "text",
             "text",
-            gr.Slider(minimum=0.0, maximum=1.0, step=0.1, default=1.0, label="Top-p value"),
+            gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="top-p value"),
             "checkbox"
         ],
         outputs="text",
@@ -119,17 +128,29 @@ def main():
                        Check the FAQs at the bottom of the page for more info.""",
         article="""**FAQs**
 
-**What's Top-p?** 'Top-p' controls the diversity of AI responses: a low 'top-p' makes output more focused and predictable, while a high 'top-p' encourages variety and surprise. Pair with temperature to fine-tune AI creativity: higher temperatures with high 'top-p' for bold ideas, or lower temperatures with low 'top-p' for precise answers.
+**What's Top-p?** 'Top-p' controls the diversity of AI responses: a low 'top-p' makes output more focused and predictable, while a high 'top-p' encourages variety and surprise. Pair with temperature to fine-tune AI creativity: higher temperatures with high 'top-p' for bold ideas, or lower temperatures with low 'top-p' for precise answers. 
+Using top_p=1 essentially disables the "nucleus sampling" feature, where only the most probable tokens are considered. This is equivalent to using full softmax probability distribution to sample the next word.
 
 **How Does Temperature Affect AI Outputs?** Temperature controls the randomness of word selection. Lower temperatures lead to more predictable text, while higher temperatures allow for more novel text generation.
 
 **How Does Top-p Influence Temperature Settings in AI Language Models?**
-Top-p and temperature are both parameters that control the randomness of AI-generated text, but they influence outcomes in subtly different ways. Here's a summary of their interaction:
+Top-p and temperature are both parameters that control the randomness of AI-generated text, but they influence outcomes in subtly different ways:
 
-- At Low Temperatures (0.0 - 0.5): High top-p has a limited effect; low top-p further narrows word choice.
-- At Medium Temperatures (0.5 - 0.7): Top-p begins to have a noticeable impact; higher top-p adds variety.
-- At High Temperatures (0.8 - 1.0): Top-p is crucial; high top-p leads to creative, less coherent outputs.
-- At Extra-High Temperatures (1.1 - 2.0): Outputs become experimental; top-p's impact is less predictable.
+- **Low Temperatures (0.0 - 0.5):**
+  - *Effect of Top-p:* A high `top_p` value will have minimal impact, as the model's output is already quite deterministic. A low `top_p` will further constrain the model, leading to very predictable outputs.
+  - *Use Cases:* Ideal for tasks requiring precise, factual responses like technical explanations or legal advice. For example, explaining a scientific concept or drafting a formal business email.
+
+- **Medium Temperatures (0.5 - 0.7):**
+  - *Effect of Top-p:* `top_p` starts to influence the variety of the output. A higher `top_p` will introduce more diversity without sacrificing coherence.
+  - *Use Cases:* Suitable for creative yet controlled content, such as writing an article on a current event or generating a business report that balances creativity with professionalism.
+
+- **High Temperatures (0.8 - 1.0):**
+  - *Effect of Top-p:* A high `top_p` is crucial for introducing creativity and surprise, but may result in less coherent outputs. A lower `top_p` can help maintain some coherence.
+  - *Use Cases:* Good for brainstorming sessions, generating creative writing prompts, or coming up with out-of-the-box ideas where a mix of novelty and relevance is appreciated.
+
+- **Extra-High Temperatures (1.1 - 2.0):**
+  - *Effect of Top-p:* The output becomes more experimental and unpredictable, and `top_p`'s influence can vary widely. It's a balance between randomness and diversity.
+  - *Use Cases:* Best for when you're seeking highly creative or abstract ideas, such as imagining a sci-fi scenario or coming up with a plot for a fantasy story, where coherence is less of a priority compared to novelty and uniqueness.
 
 Adjusting both temperature and top-p helps tailor the AI's output to your specific needs.""",
         examples=[