Create ai_processing.py

ai_processing.py

@@ -0,0 +1,43 @@
+import random
+from llama_cpp import Llama
+
+# Initialize Llama model
+llm = Llama(model_path="path/to/llama-3.2-model.bin")
+
+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:
+    """
+    
+    response = llm(prompt, max_tokens=200)
+    return response['choices'][0]['text'].strip()
+
+def calculate_fundraising_score(user_data):
+    # In a real implementation, this would use more sophisticated analysis
+    # For this example, we'll use a random score
+    return random.randint(50, 95)
+
+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 = llm(prompt, max_tokens=300)
+    recommendations = response['choices'][0]['text'].strip().split('\n')
+    return [rec.strip() for rec in recommendations if rec.strip()]