Upload app.py

app.py

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+import gradio as gr
+import requests
+import os 
+
+##Bloom Inference API
+
+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"Prompt: {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_)
+    output = response.json()
+
+    # Debug: display the output
+    Thoughts += f"Output: {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"
+
+        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,
+            "parameters":
+            {
+            "top_p": 0.9,
+          "temperature": 1.1,
+          #"max_new_tokens": 64,
+          "return_full_text": True,
+          "do_sample":True,
+          }, 
+          "options": 
+          {"use_cache": True,
+          "wait_for_model": True,
+          },}
+
+    
+  response = requests.post(API_URL, headers=headers, json=json_)
+  print(f"Response  is : {response}")
+  output = response.json()
+  print(f"output is : {output}") 
+  output_tmp = output[0]['generated_text']
+  print(f"output_tmp is: {output_tmp}")
+  solution = output_tmp.split("\nQ:")[0]   
+  print(f"Final response after splits is: {solution}")
+
+    
+  if '\nOutput:' in solution:
+    final_solution = solution.split("\nOutput:")[0] 
+    print(f"Response after removing output is: {final_solution}")
+  elif '\n\n' in solution:
+    final_solution = solution.split("\n\n")[0] 
+    print(f"Response after removing new line entries is: {final_solution}")
+  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):]
+  print(f"New prompt for next cycle: {new_prompt}")
+  print(f"Output final is : {display_output}")
+  if len(new_prompt) == 0:
+    temp_text = display_output[::-1]
+    print(f"Last character of sentence: {temp_text[0]}")
+    if temp_text[1] == '.':
+      first_period_loc = temp_text[2:].find('.') + 1
+      print(f"Location of last Period is: {first_period_loc}")
+      new_prompt = display_output[-first_period_loc:-1]
+      print(f"Not sending blank as prompt so new prompt for next cycle is : {new_prompt}")
+    else:
+      print("HERE")
+      first_period_loc = temp_text.find('.')
+      print(f"Last Period is : {first_period_loc}")
+      new_prompt = display_output[-first_period_loc:-1]
+      print(f"New prompt for next cycle is : {new_prompt}")
+    display_output = display_output[:-1]
+  return display_output, new_prompt  
+
+# An insightful and engaging self-care health care demo
+demo = gr.Blocks()
+
+with demo:
+    with gr.Row():
+        input_prompt = gr.Textbox(
+            label="Write a self-care or health care related question to get started...",
+            lines=3,
+            value="Dear AI, please tell me about the importance of self-care and how it contributes to overall health and well-being.",
+        )
+
+    with gr.Row():
+        generated_txt = gr.Textbox(lines=5, visible=True)
+
+    with gr.Row():
+        Thoughts = gr.Textbox(lines=10, visible=True)
+
+    gen = gr.Button("Discover Health Insights")
+
+    gen.click(
+        text_generate,
+        inputs=[input_prompt, generated_txt],
+        outputs=[generated_txt, input_prompt, Thoughts],
+    )
+
+demo.launch(enable_queue=True, debug=True)