feat: End-to-End Medical Report Extraction Pipeline with GGUF-LoRA Integration

.gitignore

@@ -1 +1,34 @@
 .qodo
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+venv/
+env/
+.env
+
+# Models (Extremely Important - Do not push 500MB+ files)
+**/models/*.gguf
+**/models/*.safetensors
+**/models/*.bin
+**/models/config.json
+**/models/adapter_config.json
+
+# Node / React Native
+node_modules/
+.expo/
+dist/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Mac / OS specific
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+
+# Debugging
+ocr_debug.txt
+*.logBackend/db/chromadb/chroma.sqlite3

Backend/agent/handlers/appointment.py

@@ -81,44 +81,28 @@ def parse_appointment_command(query: str):
     }
 
 def parse_date(date_str):
-    """Parse date string to ISO format."""
-    if not date_str:
-        return None
-
+    if not date_str: return None
     today = datetime.now()
-    date_str_lower = date_str.lower()
+    date_str_lower = date_str.lower().strip()
 
-    if date_str_lower == 'today':
-        return today.strftime('%Y-%m-%d')
-    elif date_str_lower == 'tomorrow':
-        return (today + timedelta(days=1)).strftime('%Y-%m-%d')
-    elif date_str_lower == 'next week':
-        return (today + timedelta(days=7)).strftime('%Y-%m-%d')
 
-    # Try to parse as MM/DD or MM/DD/YYYY
-    try:
-        if '/' in date_str:
-            parts = date_str.split('/')
-            if len(parts) == 2:
-                month, day = int(parts[0]), int(parts[1])
-                year = today.year
-                if month < today.month or (month == today.month and day < today.day):
-                    year += 1
-                return f"{year}-{month:02d}-{day:02d}"
-            elif len(parts) == 3:
-                month, day, year = int(parts[0]), int(parts[1]), int(parts[2])
-                return f"{year}-{month:02d}-{day:02d}"
-    except:
-        pass
+    if date_str_lower == 'next month':
+        next_month = (today.month % 12) + 1
+        year = today.year + (1 if today.month == 12 else 0)
+        return f"{year}-{next_month:02d}-01"
 
-    # Try to parse as YYYY-MM-DD
-    try:
-        datetime.strptime(date_str, '%Y-%m-%d')
-        return date_str
-    except:
-        pass
+    day_mapping = {
+        'monday': 0, 'mon': 0, 'tuesday': 1, 'tue': 1,
+        'wednesday': 2, 'wed': 2, 'thursday': 3, 'thu': 3, 'thurs': 3,
+        'friday': 4, 'fri': 4, 'saturday': 5, 'sat': 5, 'sunday': 6, 'sun': 6,
+    }
+
+    if date_str_lower in day_mapping:
+        days_ahead = day_mapping[date_str_lower] - today.weekday()
+        if days_ahead <= 0: days_ahead += 7
+        return (today + timedelta(days=days_ahead)).strftime('%Y-%m-%d')
 
-    return None
+    return date_str 
 
 def parse_time(time_str):
     """Parse time string to HH:MM format."""

Backend/agent/llm.py

@@ -1,55 +1,30 @@
+from llama_cpp import Llama
+
+
+llm = Llama(
+    model_path="./models/qwen2-0_5b-instruct-q4_k_m.gguf", 
+    lora_path="./models/adapter_model.bin",               
+    n_ctx=512,                                            
+    n_gpu_layers=-1                                       
+)
+
 def run_llm(prompt: str) -> str:
-    """
-    Run LLM inference. 
-
-    For the offline BabyNest app:
-    - This will be called from the frontend using Llama.rn
-    - The frontend will handle the actual LLM inference
-    - This function prepares the prompt for frontend processing
-
-    Args:
-        prompt: The formatted prompt with user context and query
-
-    Returns:
-        str: LLM response (will be replaced by frontend Llama.rn call)
-    """
-    # TODO: Replace with frontend Llama.rn integration
-    # For now, return a structured response based on the prompt content
-
-    if "weight" in prompt.lower():
-        return """Based on your weight tracking data, you're showing a healthy pattern. 
-        Your weight gain is within normal ranges for pregnancy. Continue monitoring weekly 
-        and consult your healthcare provider if you notice any sudden changes."""
-
-    elif "appointment" in prompt.lower():
-        return """I can help you manage your appointments. Based on your current week, 
-        you should focus on regular prenatal checkups. Would you like me to suggest 
-        optimal scheduling times or help reschedule any missed appointments?"""
-
-    elif "symptoms" in prompt.lower():
-        return """I see you're tracking various symptoms. This is normal during pregnancy. 
-        Continue monitoring and report any concerning symptoms to your healthcare provider. 
-        Your tracking data helps identify patterns that may need attention."""
-
-    else:
-        return """I'm here to support your pregnancy journey! Based on your current week 
-        and tracking data, you're doing well. Remember to stay hydrated, get adequate rest, 
-        and maintain regular prenatal care. Is there anything specific you'd like to know 
-        about your current pregnancy week?"""
+    """Actual inference logic for medical extraction."""
+    output = llm(
+        prompt, 
+        max_tokens=256, 
+        stop=["}"], 
+        temperature=0
+    )
+    response = output['choices'][0]['text'].strip()
+    # Ensuring valid JSON structure
+    return response + "}" if not response.endswith("}") else response
 
 def prepare_prompt_for_frontend(prompt: str) -> dict:
-    """
-    Prepare prompt for frontend Llama.rn processing.
-
-    Args:
-        prompt: The formatted prompt
-
-    Returns:
-        dict: Structured data for frontend LLM processing
-    """
+    """Prepare prompt for future frontend Llama.rn processing."""
     return {
         "prompt": prompt,
-        "max_tokens": 500,
-        "temperature": 0.7,
-        "system_message": "You are BabyNest, an empathetic pregnancy companion providing personalized, evidence-based guidance."
-    }
+        "max_tokens": 150,
+        "temperature": 0.1,
+        "system_message": "You are BabyNest, an empathetic pregnancy companion. Extract medical data into JSON."
+    }