File size: 2,697 Bytes
7ad45a6 85ea247 71d5cad 85ea247 71d5cad 85ea247 71d5cad 85ea247 71d5cad 85ea247 71d5cad |
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 |
import pytorch
from transformers import MllamaForConditionalGeneration, AutoProcessor
# Initialize Llama model
model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)
def generate_text(prompt, max_new_tokens=200):
messages = [
{"role": "user", "content": [
{"type": "text", "text": prompt}
]}
]
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(
input_text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
output = model.generate(**inputs, max_new_tokens=max_new_tokens)
return processor.decode(output[0], skip_special_tokens=True)
def generate_company_profile(user_data):
prompt = f"""
Generate a concise company profile based on the following information:
Project Description: {user_data['project_description']}
Industry: {user_data['industry']}
Target Market: {user_data['market']}
Location: {user_data['location']}
Founders: {', '.join([f['name'] for f in user_data['founders_info']])}
Company Profile:
"""
return generate_text(prompt, max_new_tokens=200)
def calculate_fundraising_score(user_data):
prompt = f"""
Based on the following company information, provide a fundraising probability score between 0 and 100:
Project Description: {user_data['project_description']}
Industry: {user_data['industry']}
Target Market: {user_data['market']}
Location: {user_data['location']}
Number of Founders: {len(user_data['founders_info'])}
Fundraising Probability Score (0-100):
"""
response = generate_text(prompt, max_new_tokens=10)
try:
score = int(response.strip())
return max(0, min(100, score)) # Ensure the score is between 0 and 100
except ValueError:
return 50 # Default score if parsing fails
def generate_recommendations(user_data):
prompt = f"""
Based on the following company information, provide 3-5 recommendations to improve fundraising success:
Project Description: {user_data['project_description']}
Industry: {user_data['industry']}
Target Market: {user_data['market']}
Location: {user_data['location']}
Number of Founders: {len(user_data['founders_info'])}
Recommendations:
"""
response = generate_text(prompt, max_new_tokens=300)
recommendations = response.strip().split('\n')
return [rec.strip() for rec in recommendations if rec.strip()] |