Update ai_processing.py

ai_processing.py

@@ -1,8 +1,30 @@
-import random
-from llama_cpp import Llama
+import torch
+from transformers import MllamaForConditionalGeneration, AutoProcessor
 
 # Initialize Llama model
-llm = Llama(model_path="path/to/llama-3.2-model.bin")
+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"""
@@ -17,13 +39,27 @@ def generate_company_profile(user_data):
     Company Profile:
     """
     
-    response = llm(prompt, max_tokens=200)
-    return response['choices'][0]['text'].strip()
+    return generate_text(prompt, max_new_tokens=200)
 
 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)
+    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"""
@@ -38,6 +74,6 @@ def generate_recommendations(user_data):
     Recommendations:
     """
     
-    response = llm(prompt, max_tokens=300)
-    recommendations = response['choices'][0]['text'].strip().split('\n')
+    response = generate_text(prompt, max_new_tokens=300)
+    recommendations = response.strip().split('\n')
     return [rec.strip() for rec in recommendations if rec.strip()]