Update app.py

app.py

@@ -3,12 +3,96 @@ import requests
 import os 
 
 ##Bloom Inference API
-API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-HF_TOKEN = os.environ["HF_TOKEN"]
+
+API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" # Models on HF feature inference API which allows direct call and easy interface
+
+HF_TOKEN = os.environ["HF_TOKEN"] # Add a token called HF_TOKEN under profile in settings access tokens.  Then copy it to the repository secret in this spaces settings panel.  os.environ reads from there.
+
+# For headers the bearer token needs to incclude your HF_TOKEN value.
 headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 
+# Improved text generation function
+def text_generate(prompt, generated_txt):
+    # Initialize Thoughts variable to aggregate text
+    Thoughts = ""
+
+    # Debug: display the prompt
+    Thoughts += f"*****Inside text_generate - Prompt is: {prompt}\n"
+
+    json_ = {
+        "inputs": prompt,
+        "parameters": {
+            "top_p": 0.9,
+            "temperature": 1.1,
+            "return_full_text": True,
+            "do_sample": True,
+        },
+        "options": {
+            "use_cache": True,
+            "wait_for_model": True,
+        },
+    }
+    response = requests.post(API_URL, headers=headers, json=json_)
+
+    # Debug: display the response
+    Thoughts += f"Response is: {response}\n"
+    output = response.json()
+
+    # Debug: display the output
+    Thoughts += f"output is: {output}\n"
+    output_tmp = output[0]['generated_text']
+
+    # Debug: display the output_tmp
+    Thoughts += f"output_tmp is: {output_tmp}\n"
+    solution = output_tmp.split("\nQ:")[0]
+
+    # Debug: display the solution after splitting
+    Thoughts += f"Final response after splits is: {solution}\n"
+
+    if '\nOutput:' in solution:
+        final_solution = solution.split("\nOutput:")[0]
+        Thoughts += f"Response after removing output is: {final_solution}\n"
+    elif '\n\n' in solution:
+        final_solution = solution.split("\n\n")[0]
+        Thoughts += f"Response after removing new line entries is: {final_solution}\n"
+    else:
+        final_solution = solution
+
+    if len(generated_txt) == 0:
+        display_output = final_solution
+    else:
+        display_output = generated_txt[:-len(prompt)] + final_solution
+
+    new_prompt = final_solution[len(prompt):]
+
+    # Debug: display the new prompt for the next cycle
+    Thoughts += f"new prompt for next cycle is: {new_prompt}\n"
+    Thoughts += f"display_output for printing on screen is: {display_output}\n"
+
+    if len(new_prompt) == 0:
+        temp_text = display_output[::-1]
+        Thoughts += f"What is the last character of the sentence?: {temp_text[0]}\n"
+
+        if temp_text[1] == '.':
+            first_period_loc = temp_text[2:].find('.') + 1
+            Thoughts += f"Location of last Period is: {first_period_loc}\n"
+            new_prompt = display_output[-first_period_loc:-1]
+            Thoughts += f"Not sending blank as prompt so new prompt for next cycle is: {new_prompt}\n"
+        else:
+            first_period_loc = temp_text.find('.')
+            Thoughts += f"Location of last Period is: {first_period_loc}\n"
+            new_prompt = display_output[-first_period_loc:-1]
+            Thoughts += f"Not sending blank as prompt so new prompt for next cycle is: {new_prompt}\n"
 
-def text_generate(prompt, generated_txt): 
+        display_output = display_output[:-1]
+
+    return display_output, new_prompt, Thoughts
+
+
+
+    
+# Text generation
+def text_generate_old(prompt, generated_txt): 
   #Prints to debug the code
   print(f"*****Inside text_generate - Prompt is :{prompt}")
   json_ = {"inputs": prompt,
@@ -71,18 +155,17 @@ def text_generate(prompt, generated_txt):
 demo = gr.Blocks()
 
 with demo:
-  gr.Markdown("<h1><center>Write Stories Using Bloom</center></h1>")
-  gr.Markdown(
-        """Bloom is a model by [HuggingFace](https://huggingface.co/bigscience/bloom) and a team of more than 1000 researchers coming together as [BigScienceW Bloom](https://twitter.com/BigscienceW).\n\nLarge language models have demonstrated a capability of producing coherent sentences and given a context we can pretty much decide the *theme* of generated text.\n\nHow to Use this App: Use the sample text given as prompt or type in a new prompt as a starting point of your awesome story! Just keep pressing the 'Generate Text' Button and go crazy!\n\nHow this App works: This app operates by feeding back the text generated by Bloom to itself as a Prompt for next generation round and so on. Currently, due to size-limits on Prompt and Token generation, we are only able to feed very limited-length text as Prompt and are getting very few tokens generated in-turn. This makes it difficult to keep a tab on theme of text generation, so please bear with that. In summary, I believe it is a nice little fun App which you can play with for a while.\n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for EuroPython 2022 Demo."""
-        )
   with gr.Row():
     input_prompt = gr.Textbox(label="Write some text to get started...", lines=3, value="Dear human philosophers, I read your comments on my abilities and limitations with great interest.")  
     
   with gr.Row():
-    generated_txt = gr.Textbox(lines=7, visible = True)
+    generated_txt = gr.Textbox(lines=4, visible = True)  
+      
+  with gr.Row():
+    Thoughts = gr.Textbox(lines=4, visible = True)
   
   b1 = gr.Button("Generate Your Story")
 
-  b1.click(text_generate, inputs=[input_prompt, generated_txt], outputs=[generated_txt, input_prompt]) 
+  b1.click(text_generate, inputs=[input_prompt, generated_txt], outputs=[generated_txt, input_prompt, Thoughts]) 
 
 demo.launch(enable_queue=True, debug=True)