Spaces:
Sleeping
Sleeping
File size: 9,847 Bytes
e60fb94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
import google.generativeai as genai
from flask import Response, stream_with_context
import json
import time
def craft_notebook_prompt(user_prompt):
"""Enhance the user prompt with instructions for generating a well-structured Jupyter notebook."""
enhanced_prompt = f"""
Create a complete Jupyter notebook based on this request: "{user_prompt}"
Please structure your response as follows:
NOTEBOOK_NAME: [Short, descriptive name for the notebook with no formating "**"]
NOTEBOOK_DESCRIPTION: [a description of the notebook's purpose with no formating]
Then provide the complete notebook with proper alternating Markdown and code cells.
Format each cell as follows:
--- MARKDOWN CELL ---
[Markdown content here]
--- CODE CELL ---
```python
[Python code here]
```
Important guidelines:
- Include comprehensive explanations in Markdown cells
- Ensure all code is executable and properly commented
- Include data loading, processing, and visualization where appropriate
- Add explanatory text before and after code sections
- Include example outputs or expected results when relevant
- Structure the notebook with clear section headers in Markdown
"""
return enhanced_prompt
def craft_edit_prompt(edit_request, notebook_json):
"""Create a prompt for editing an existing notebook."""
# Extract cells from notebook JSON for context
cells = []
for i, cell in enumerate(notebook_json.get('cells', [])):
cell_type = cell.get('cell_type', 'unknown')
source = cell.get('source', [])
if isinstance(source, list):
source = '\n'.join(source)
if cell_type == 'markdown':
cells.append(f"--- CELL {i+1} (MARKDOWN) ---\n{source}")
elif cell_type == 'code':
cells.append(f"--- CELL {i+1} (CODE) ---\n```python\n{source}\n```")
notebook_content = '\n\n'.join(cells)
# Create prompt with edit instructions
enhanced_prompt = f"""
I have a Jupyter notebook that I'd like you to modify based on this edit request: "{edit_request}"
Here's the current notebook content:
NOTEBOOK_STRUCTURE:
{notebook_content}
Please provide the complete updated notebook with the requested changes, following the same format:
NOTEBOOK_NAME: [Keep or update the notebook name]
NOTEBOOK_DESCRIPTION: [Keep or update the notebook description]
Then provide the complete notebook with proper alternating Markdown and code cells.
Format each cell as follows:
--- MARKDOWN CELL ---
[Markdown content here]
--- CODE CELL ---
```python
[Python code here]
```
Important guidelines:
- Make only the changes requested in the edit request
- Preserve the overall structure of the notebook
- Keep all content from the original notebook that doesn't need modification
- Ensure all code remains executable and properly commented
- Feel free to reorganize, add, or remove cells as needed to fulfill the edit request
"""
return enhanced_prompt
def generate_notebook(user_prompt, model_name="gemini-2.0-pro-exp-02-05"):
"""Generate a complete notebook using Gemini API."""
model = genai.GenerativeModel(model_name)
enhanced_prompt = craft_notebook_prompt(user_prompt)
response = model.generate_content(enhanced_prompt)
return response.text
def edit_notebook(edit_request, notebook_json, model_name="gemini-2.0-pro-exp-02-05"):
"""Edit an existing notebook based on user request."""
model = genai.GenerativeModel(model_name)
enhanced_prompt = craft_edit_prompt(edit_request, notebook_json)
response = model.generate_content(enhanced_prompt)
return response.text
def stream_notebook_generation(user_prompt, model_name="gemini-2.0-pro-exp-02-05"):
"""Stream notebook generation responses from Gemini API."""
model = genai.GenerativeModel(model_name)
enhanced_prompt = craft_notebook_prompt(user_prompt)
def generate():
try:
response = model.generate_content(enhanced_prompt, stream=True)
# Send a notification that streaming has started
yield f"data: {json.dumps({'chunk': 'Starting notebook generation...'})}\n\n"
for chunk in response:
try:
# More robust empty chunk detection
if not hasattr(chunk, 'parts') or not chunk.parts:
# Skip this empty chunk and continue
print("Warning: Empty chunk received (no parts)")
continue
# First try the standard text property
try:
if hasattr(chunk, 'text') and chunk.text:
yield f"data: {json.dumps({'chunk': chunk.text})}\n\n"
continue # If we successfully got text, continue to next chunk
except (AttributeError, IndexError):
# If accessing text property fails, we'll try extracting from parts
pass
# If we're here, we couldn't get text directly, try to extract from parts
for part in chunk.parts:
# Extract text from part using different approaches
if hasattr(part, 'text') and part.text:
yield f"data: {json.dumps({'chunk': part.text})}\n\n"
elif isinstance(part, dict) and 'text' in part:
yield f"data: {json.dumps({'chunk': part['text']})}\n\n"
elif hasattr(part, 'string_value'):
yield f"data: {json.dumps({'chunk': part.string_value})}\n\n"
except (AttributeError, IndexError, TypeError) as e:
# Log the error but continue - don't break the stream
print(f"Error processing chunk: {e}, chunk structure: {repr(chunk)[:200]}")
continue
# Briefly pause to prevent overwhelming the client
time.sleep(0.01)
yield f"data: {json.dumps({'done': True})}\n\n"
except Exception as e:
# Send error to client and close stream
error_message = f"Error generating notebook: {str(e)}"
print(error_message)
yield f"data: {json.dumps({'error': error_message})}\n\n"
yield f"data: {json.dumps({'done': True})}\n\n"
return Response(stream_with_context(generate()), content_type="text/event-stream")
def stream_notebook_edit(edit_request, notebook_json, model_name="gemini-2.0-pro-exp-02-05"):
"""Stream notebook editing responses from Gemini API."""
model = genai.GenerativeModel(model_name)
enhanced_prompt = craft_edit_prompt(edit_request, notebook_json)
def generate():
try:
response = model.generate_content(enhanced_prompt, stream=True)
# Send a notification that editing has started
yield f"data: {json.dumps({'chunk': 'Starting notebook edit...'})}\n\n"
for chunk in response:
try:
# More robust empty chunk detection
if not hasattr(chunk, 'parts') or not chunk.parts:
# Skip this empty chunk and continue
print("Warning: Empty chunk received (no parts)")
continue
# First try the standard text property
try:
if hasattr(chunk, 'text') and chunk.text:
yield f"data: {json.dumps({'chunk': chunk.text})}\n\n"
continue # If we successfully got text, continue to next chunk
except (AttributeError, IndexError):
# If accessing text property fails, we'll try extracting from parts
pass
# If we're here, we couldn't get text directly, try to extract from parts
for part in chunk.parts:
# Extract text from part using different approaches
if hasattr(part, 'text') and part.text:
yield f"data: {json.dumps({'chunk': part.text})}\n\n"
elif isinstance(part, dict) and 'text' in part:
yield f"data: {json.dumps({'chunk': part['text']})}\n\n"
elif hasattr(part, 'string_value'):
yield f"data: {json.dumps({'chunk': part.string_value})}\n\n"
except (AttributeError, IndexError, TypeError) as e:
# Log the error but continue - don't break the stream
print(f"Error processing chunk: {e}, chunk structure: {repr(chunk)[:200]}")
continue
# Briefly pause to prevent overwhelming the client
time.sleep(0.01)
yield f"data: {json.dumps({'done': True})}\n\n"
except Exception as e:
# Send error to client and close stream
error_message = f"Error editing notebook: {str(e)}"
print(error_message)
yield f"data: {json.dumps({'error': error_message})}\n\n"
yield f"data: {json.dumps({'done': True})}\n\n"
return Response(stream_with_context(generate()), content_type="text/event-stream")
|