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SYSTEM_OVERVIEW.md

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+# TerraSyncra AI – Product & System Overview
+
+## 1. Product Introduction
+
+**TerraSyncra** is a multilingual agricultural intelligence agent designed specifically for Nigerian (and African) farmers. It provides comprehensive agricultural support through AI-powered assistance.
+
+**Key Capabilities:**
+- **Agricultural Q&A**: Answers questions about crops, livestock, soil, weather, pests, and diseases in multiple languages
+- **Soil Analysis**: Provides expert soil health assessments from lab reports and field data using Gemini 3 Flash
+- **Disease Detection**: Identifies plant and animal diseases from images, text descriptions, or voice input using Gemini 2.5 Flash
+- **Live Agricultural Updates**: Delivers real-time weather information and agricultural news through RAG (Retrieval-Augmented Generation)
+- **Live Voice Interaction**: Supports real-time voice conversations via WebSocket in local languages (Igbo, Hausa, Yoruba, English)
+
+**Developer**: Ifeanyi Amogu Shalom  
+**Target Users**: Farmers, agronomists, agricultural extension officers, and agricultural support workers in Nigeria and similar contexts
+
+---
+
+## 2. Problem Statement
+
+Nigerian smallholder farmers face significant challenges:
+
+### 2.1 Limited Access to Agricultural Experts
+- **Scarcity of agronomists and veterinarians** relative to the large farming population
+- **Geographic barriers** preventing farmers from accessing expert advice
+- **High consultation costs** that many smallholder farmers cannot afford
+- **Long waiting times** for professional consultations, especially during critical periods (disease outbreaks, planting seasons)
+
+### 2.2 Language Barriers
+- Most agricultural information and resources are in **English**, while many farmers primarily speak **Hausa, Igbo, or Yoruba**
+- **Technical terminology** is not easily accessible in local languages
+- **Translation services** are often unavailable or unreliable
+
+### 2.3 Fragmented Information Sources
+- Weather data, soil reports, disease information, and market prices are scattered across different platforms
+- **No unified system** to integrate and interpret multiple data sources
+- **Information overload** without proper context or prioritization
+
+### 2.4 Time-Sensitive Decision Making
+- **Disease outbreaks** require immediate identification and treatment
+- **Weather changes** affect planting, harvesting, and irrigation decisions
+- **Pest attacks** can devastate crops if not addressed quickly
+- **Delayed responses** lead to significant economic losses
+
+### 2.5 Solution Approach
+TerraSyncra addresses these challenges by providing:
+- **Fast, AI-powered responses** available 24/7
+- **Multilingual support** (English, Igbo, Hausa, Yoruba)
+- **Integrated intelligence** combining expert models, RAG, and live data
+- **Accessible interface** via text, voice, and image inputs
+- **Professional consultation reminders** to ensure farmers seek expert confirmation when needed
+
+---
+
+## 3. System Architecture & Request Flows
+
+### 3.1 General Agricultural Q&A – `POST /ask`
+
+**Step-by-Step Process:**
+
+1. **Input Reception**
+   - User sends `query` (text) with optional `session_id` for conversation continuity
+
+2. **Language Detection**
+   - FastText model (`facebook/fasttext-language-identification`) detects input language
+   - Supports: English, Igbo, Hausa, Yoruba
+
+3. **Translation (if needed)**
+   - If language ≠ English, translates to English using NLLB (`drrobot9/nllb-ig-yo-ha-finetuned`)
+   - Preserves original language for back-translation
+
+4. **Intent Detection**
+   - Classifies query into categories:
+     - **Weather question**: Requests weather information (with/without Nigerian state)
+     - **Live update**: Requests current agricultural news or updates
+     - **Normal question**: General agricultural Q&A
+     - **Low confidence**: Falls back to RAG when intent is unclear
+
+5. **Context Building**
+   - **Weather intent**: Calls WeatherAPI for state-specific weather data, embeds summary into context
+   - **Live update intent**: Queries live FAISS vectorstore index for latest agricultural documents
+   - **Low confidence**: Falls back to static FAISS index for safer, more general responses
+
+6. **Conversation Memory**
+   - Loads per-session history from `MemoryStore` (TTL cache, 1-hour expiration)
+   - Trims to `MAX_HISTORY_MESSAGES` (default: 30) to prevent context overflow
+
+7. **Expert Model Generation**
+   - Uses **Qwen/Qwen1.5-1.8B** (finetuned for Nigerian agriculture)
+   - Loaded lazily via `model_manager` (CPU-optimized, first-use loading)
+   - Builds chat messages: system prompt + conversation history + current user message + context
+   - System prompt restricts responses to **agriculture/farming topics only**
+   - Generates bounded-length answer (reduced token limit: 400 tokens for general, 256 for weather)
+   - Cleans response to remove any "Human: / Assistant:" style example continuations
+
+8. **Back-Translation**
+   - If original language ≠ English, translates answer back to user's language using NLLB
+
+9. **Response**
+   - Returns JSON: `{ query, answer, session_id, detected_language }`
+
+**Safety & Focus:**
+- System prompt enforces agriculture-only topic handling
+- Unrelated questions are redirected back to farming topics
+- Response cleaning prevents off-topic example continuations
+
+---
+
+### 3.2 Soil Analysis – `POST /analyze-soil`
+
+**Step-by-Step Process:**
+
+1. **Input Reception**
+   - `report_data`: Text description of soil report or lab results (required)
+   - Optional fields: `location`, `crop_type`, `field_size`, `previous_crops`, `additional_notes`
+
+2. **Agent Processing**
+   - `soil_agent.analyze_soil()` builds comprehensive prompt with:
+     - Soil report data
+     - Field information (location, crop type, size, history)
+     - Regional context (Nigerian states, climate patterns)
+
+3. **Gemini API Call**
+   - Model: `GEMINI_SOIL_MODEL = "gemini-3-flash-preview"`
+   - Prompt style: Brief, direct, actionable
+   - Focuses on:
+     - Current soil condition (short summary)
+     - Key nutrient issues (deficiencies or excesses)
+     - 1–3 best crops for this soil type
+     - Clear fertilizer and amendment recommendations
+     - Simple soil improvement steps
+
+4. **Output**
+   - JSON response: `{ success, analysis, model_used }`
+
+**Important Note:**
+> Soil analysis is **advisory only** – not a formal agronomy diagnosis. The UI should encourage farmers to confirm with a local agronomist or extension officer for critical decisions.
+
+---
+
+### 3.3 Disease Detection
+
+#### 3.3.1 Image-Based Detection – `POST /detect-disease-image`
+
+**Step-by-Step Process:**
+
+1. **Input Reception**
+   - Image file (JPEG, PNG, etc.)
+   - Optional `query`: Text description or question
+
+2. **Agent Processing**
+   - `disease_agent.classify_disease_from_image()` processes:
+     - Image bytes + MIME type
+     - User query (if provided)
+     - Builds structured prompt for Gemini
+
+3. **Gemini API Call**
+   - Model: `GEMINI_DISEASE_MODEL = "gemini-2.5-flash"`
+   - Prompt instructs Gemini to provide:
+     - Disease name (scientific + common name) in 1 short line
+     - **Threat level: Low / Moderate / High / Uncertain** (MANDATORY)
+     - 2–3 key symptoms visible in image
+     - 2–3 clear treatment steps (bullets)
+     - 1–2 simple prevention tips
+     - Brief, direct language with short sentences
+
+4. **Backend Safety Enforcement**
+   - Backend **always appends** disclaimer:
+     > "IMPORTANT: This threat level is an estimate based only on the image/description. For an accurate diagnosis and treatment plan, please consult a qualified agronomist, veterinary doctor, or local agricultural extension officer."
+
+5. **Output**
+   - JSON response: `{ success, classification, model_used, input_type }`
+
+#### 3.3.2 Text/Voice-Based Detection – `POST /detect-disease-text`
+
+**Step-by-Step Process:**
+
+1. **Input Reception**
+   - `description`: Text description of disease symptoms or condition
+   - `language`: Language code (en, ig, ha, yo)
+
+2. **Agent Processing**
+   - `disease_agent.classify_disease_from_text()` processes:
+     - Text description
+     - Language context
+     - Builds structured prompt for Gemini
+
+3. **Gemini API Call**
+   - Same model and prompt structure as image-based detection
+   - Threat level assessment based on described symptoms
+
+4. **Backend Safety Enforcement**
+   - Same disclaimer appended as image-based detection
+
+5. **Output**
+   - JSON response: `{ success, classification, model_used, input_type }`
+
+**Threat Level Guidelines:**
+- **Low**: Mild or early-stage issue, unlikely to cause major losses if addressed soon
+- **Moderate**: Noticeable risk that can reduce yield/health if not treated
+- **High**: Serious or fast-spreading issue that can cause major losses or death (use cautiously, only when clearly severe)
+- **Uncertain**: Insufficient or ambiguous data; model cannot safely rate risk (encouraged when not confident)
+
+---
+
+### 3.4 Live Voice Interaction – `WS /live-voice` & `POST /live-voice-start`
+
+**Step-by-Step Process:**
+
+1. **WebSocket Connection**
+   - Client connects to `/live-voice` endpoint
+   - Optional: Send image as JSON (base64 encoded) at session start
+   - Audio chunks streamed as raw PCM bytes (16kHz, mono, 16-bit)
+
+2. **Agent Processing**
+   - `live_voice_agent.handle_live_voice_websocket()` manages:
+     - WebSocket connection lifecycle
+     - Image context (if provided)
+     - Audio streaming to Gemini Live API
+     - Audio response streaming back to client
+
+3. **Gemini Live API**
+   - Model: `gemini-2.5-flash` via Gemini Live API
+   - System prompt: Brief, clear, focused on "what to do next" (2–4 key steps)
+   - Supports: Disease detection, soil analysis, general farming, weather
+   - Prefers short sentences and bullet points
+
+4. **Response Streaming**
+   - Audio responses streamed back as PCM bytes
+   - Optional JSON messages for status/transcripts
+
+5. **Safety Expectations**
+   - Same professional advice principle applies
+   - Frontends should display clear "not a replacement for a professional" banner
+
+---
+
+## 4. Technologies Used
+
+### 4.1 Backend Framework & Infrastructure
+- **FastAPI**: Modern Python web framework for building REST APIs and WebSocket endpoints
+- **Uvicorn**: ASGI server for running FastAPI applications
+- **Python 3.10**: Programming language
+- **Docker**: Containerization for deployment
+- **Hugging Face Spaces**: Deployment platform (Docker runtime, CPU-only environment)
+
+### 4.2 Core Language Models
+
+#### 4.2.1 Expert Model: Qwen/Qwen1.5-1.8B
+- **Model**: `Qwen/Qwen1.5-1.8B` (via Hugging Face Transformers)
+- **Purpose**: Primary agricultural Q&A and conversation
+- **Specialization**: **Finetuned/specialized** for Nigerian agricultural context through:
+  - Custom system prompts focused on Nigerian farming practices
+  - Domain-specific training data integration
+  - Response formatting optimized for agricultural advice
+- **Optimization**: 
+  - Lazy loading via `model_manager` (loads on first use)
+  - CPU-optimized inference (float32, device_map="cpu")
+  - Reduced token limits to prevent over-generation
+
+#### 4.2.2 Gemini Models (Google AI)
+- **google-genai**: Official Python client for Google's Gemini API
+- **gemini-3-flash-preview**: Used for soil analysis
+- **gemini-2.5-flash**: Used for disease detection and live voice interaction
+- **API Version**: v1alpha for advanced features (disease detection, live voice)
+
+### 4.3 Retrieval-Augmented Generation (RAG)
+
+- **LangChain**: Framework for building LLM applications
+- **LangChain Community**: Community integrations and tools
+- **SentenceTransformers**: 
+  - Model: `paraphrase-multilingual-MiniLM-L12-v2`
+  - Purpose: Text embeddings for semantic search
+- **FAISS (Facebook AI Similarity Search)**: 
+  - Vector database for efficient similarity search
+  - Two indices: Static (general knowledge) and Live (current updates)
+- **APScheduler**: Background job scheduler for periodic RAG updates
+
+### 4.4 Language Processing
+
+- **FastText**: 
+  - Model: `facebook/fasttext-language-identification`
+  - Purpose: Language detection (English, Igbo, Hausa, Yoruba)
+- **NLLB (No Language Left Behind)**: 
+  - Model: `drrobot9/nllb-ig-yo-ha-finetuned`
+  - Purpose: Translation between English and Nigerian languages (Hausa, Igbo, Yoruba)
+  - Bidirectional translation support
+
+### 4.5 External APIs & Data Sources
+
+- **WeatherAPI**: 
+  - Provides state-level weather data for Nigerian states
+  - Real-time weather information integration
+- **AgroNigeria / HarvestPlus**: 
+  - Agricultural news feeds for RAG updates
+  - News scraping and processing
+
+### 4.6 Additional Libraries
+
+- **transformers**: Hugging Face library for loading and using transformer models
+- **torch**: PyTorch (CPU-optimized version)
+- **numpy**: Numerical computing
+- **requests**: HTTP library for API calls
+- **beautifulsoup4**: Web scraping for news aggregation
+- **python-multipart**: File upload support for FastAPI
+- **python-dotenv**: Environment variable management
+
+---
+
+## 5. Threat Level & Safety Policy
+
+### 5.1 Domain Scope
+- **Plant and animal diseases only** – **NOT human health**
+- Focuses on agricultural and veterinary contexts
+- Does not provide medical advice for humans
+
+### 5.2 Threat Level Categories
+
+#### Low
+- **Definition**: Mild or early-stage issue, unlikely to cause major losses if addressed soon
+- **Characteristics**: 
+  - Localized symptoms
+  - Slow progression
+  - Easily manageable with standard treatments
+- **Example**: Minor leaf spots, early nutrient deficiency
+
+#### Moderate
+- **Definition**: Noticeable risk that can reduce yield/health if not treated
+- **Characteristics**:
+  - Moderate spread or impact
+  - Requires timely intervention
+  - Can cause economic losses if ignored
+- **Example**: Moderate pest infestation, developing fungal infection
+
+#### High
+- **Definition**: Serious or fast-spreading issue that can cause major losses or death
+- **Characteristics**:
+  - Rapid spread or severe symptoms
+  - High potential for significant economic impact
+  - May require immediate professional intervention
+- **Example**: Severe bacterial blight, fast-spreading viral disease
+- **Usage Caution**: Only assigned when signs are **clearly severe** or fast-spreading
+
+#### Uncertain
+- **Definition**: Insufficient or ambiguous data; model cannot safely rate risk
+- **Characteristics**:
+  - Unclear symptoms
+  - Multiple possible diagnoses
+  - Poor image quality or vague description
+- **Usage**: Encouraged when model is not confident – **better to be uncertain than wrong**
+
+### 5.3 Accuracy & Caution Approach
+
+**Threat Level Assessment:**
+- Based **only** on image + description – **no lab tests or physical examination**
+- Prompts instruct Gemini to be **conservative and cautious**
+- Model encouraged to use `Uncertain` when not clearly sure
+- Final responses always embed a strong "consult professionals" reminder
+
+**Professional Consultation Reminder:**
+- Backend **always appends** disclaimer to disease detection responses
+- Frontends should visually emphasize: "This is not a medical/veterinary/agronomic diagnosis"
+- System is a **decision-support tool**, not a definitive diagnostic engine
+
+**Important Note:**
+> **This system is a decision-support tool, not a definitive diagnosis engine.**  
+> All disease/threat outputs must be treated as preliminary guidance only.  
+> Farmers should always consult qualified professionals for critical decisions.
+
+---
+
+## 6. Limitations & Issues Faced
+
+### 6.1 Diagnostic Limitations
+
+#### Input Quality Dependencies
+- **Image Quality**: Blurry, poorly lit, or low-resolution images reduce accuracy
+- **Description Clarity**: Vague or incomplete symptom descriptions limit diagnostic precision
+- **Context Missing**: Lack of field history, crop variety, or environmental conditions affects recommendations
+
+#### Inherent Limitations
+- **No Physical Examination**: Cannot inspect internal plant structures or perform lab tests
+- **No Real-Time Monitoring**: Cannot track disease progression over time
+- **Regional Variations**: Some regional diseases may be under-represented in training data
+- **Seasonal Factors**: Disease presentation may vary by season, which may not always be captured
+
+### 6.2 Language & Translation Challenges
+
+#### Translation Accuracy
+- **NLLB Limitations**: Can misread slang, mixed-language (e.g., Pidgin + Hausa), or regional dialects
+- **Technical Terminology**: Agricultural terms may not have direct translations, leading to approximations
+- **Context Loss**: Subtle meaning can be lost across translation steps (user language → English → user language)
+
+#### Language Detection
+- **FastText Edge Cases**: May misclassify mixed-language inputs or code-switching
+- **Dialect Variations**: Regional variations within languages may not be fully captured
+
+### 6.3 Model Behavior Issues
+
+#### Hallucination Risk
+- **Qwen/Gemini Limitations**: Can generate confident but incorrect answers
+- **Mitigations Applied**:
+  - Stricter system prompts with domain restrictions
+  - Shorter output limits (400 tokens for general, 256 for weather)
+  - Response cleaning to remove example continuations
+  - Topic redirection for unrelated questions
+- **Not Bulletproof**: Hallucination can still occur, especially for edge cases
+
+#### Response Drift
+- **Off-Topic Continuations**: Models may continue with example conversations or unrelated content
+- **Mitigation**: Response cleaning logic removes "Human: / Assistant:" patterns and unrelated content
+
+### 6.4 Latency & Compute Constraints
+
+#### First-Request Latency
+- **Model Loading**: First Qwen/NLLB call is slower due to model + weights loading on CPU
+- **Cold Start**: ~5-10 seconds for first request after deployment
+- **Subsequent Requests**: Faster due to cached models in memory
+
+#### CPU-Only Environment
+- **Inference Speed**: CPU inference is slower than GPU (acceptable for Hugging Face Spaces CPU tier)
+- **Memory Constraints**: Limited RAM requires careful model management (lazy loading, model caching)
+
+### 6.5 External Dependencies
+
+#### WeatherAPI Issues
+- **Outages**: WeatherAPI downtime affects weather-related responses
+- **Rate Limits**: API quota limits may restrict frequent requests
+- **Data Accuracy**: Weather data quality depends on third-party provider
+
+#### News Source Reliability
+- **Scraping Fragility**: News sources may change HTML structure, breaking scrapers
+- **Update Frequency**: RAG updates are scheduled; failures can cause stale information
+- **Content Quality**: News article quality and relevance vary
+
+### 6.6 RAG & Data Freshness
+
+#### Update Scheduling
+- **Periodic Updates**: RAG indices updated on schedule (not real-time)
+- **Job Failures**: If update job fails, index can lag behind real-world events
+- **Index Rebuilding**: Full index rebuilds can be time-consuming
+
+#### Vectorstore Limitations
+- **Embedding Quality**: Semantic search quality depends on embedding model performance
+- **Retrieval Accuracy**: Retrieved documents may not always be most relevant
+- **Context Window**: Limited context window may truncate important information
+
+### 6.7 Deployment & Infrastructure
+
+#### Hugging Face Spaces Constraints
+- **CPU-Only**: No GPU acceleration available
+- **Memory Limits**: Limited RAM requires optimization (lazy loading, model size reduction)
+- **Build Time**: Docker builds can be slow, especially with large dependencies
+- **Cold Starts**: Spaces may spin down after inactivity, causing cold start delays
+
+#### Docker Build Issues
+- **Dependency Conflicts**: Some Python packages may conflict (e.g., pyaudio requiring system libraries)
+- **Build Timeouts**: Long build times may cause deployment failures
+- **Cache Management**: Docker layer caching can be inconsistent
+
+---
+
+## 7. Recommended UX & Safety Reminders
+
+### 7.1 Visual Disclaimers
+
+**Always display a clear banner near disease/soil results:**
+
+> "⚠️ **This is AI-generated guidance. Always confirm with a local agronomist, veterinary doctor, or agricultural extension officer before taking major actions.**"
+
+### 7.2 Threat Level Display
+
+- **Visual Highlighting**: Display threat level prominently with color coding:
+  - 🟢 **Low**: Green
+  - 🟡 **Moderate**: Yellow
+  - 🔴 **High**: Red
+  - ⚪ **Uncertain**: Gray
+- **Tooltips**: Provide explanations for each threat level
+- **Always Pair with Disclaimer**: Never show threat level without the professional consultation reminder
+
+### 7.3 Call-to-Action Buttons
+
+Provide quick access to professional help:
+- **"Contact an Extension Officer"** button/link
+- **"Find a Vet/Agronomist Near You"** button/link
+- **"Schedule a Consultation"** option (if available)
+
+### 7.4 Response Quality Indicators
+
+- Show **confidence indicators** when available (e.g., "High confidence" vs "Uncertain")
+- Display **input quality warnings** (e.g., "Image quality may affect accuracy")
+- Provide **feedback mechanisms** for users to report incorrect diagnoses
+
+### 7.5 Language Support
+
+- Clearly indicate **detected language** in responses
+- Provide **language switcher** for users to change language preference
+- Show **translation quality warnings** if translation may be approximate
+
+---
+
+## 8. System Summary
+
+### 8.1 Problem Addressed
+
+Nigerian smallholder farmers face critical challenges:
+- **Limited access to agricultural experts** (agronomists, veterinarians)
+- **Language barriers** (most resources in English, farmers speak Hausa/Igbo/Yoruba)
+- **Fragmented information sources** (weather, soil, disease data scattered)
+- **Time-sensitive decision making** (disease outbreaks, weather changes, pest attacks)
+
+### 8.2 Solution Provided
+
+TerraSyncra combines multiple AI technologies to provide:
+- **Fast, 24/7 AI-powered responses** in multiple languages
+- **Integrated intelligence**:
+  - **Finetuned Qwen 1.8B** expert model for agricultural Q&A
+  - **Gemini 3/2.5 Flash** for soil analysis and disease detection
+  - **RAG + Weather + News** for live, contextual information
+- **CPU-optimized, multilingual backend** (FastAPI on Hugging Face Spaces)
+- **Multiple input modalities**: Text, voice, and image support
+
+### 8.3 Safety & Professional Consultation
+
+**Every disease assessment includes:**
+- Explicit **Threat level** (Low / Moderate / High / Uncertain)
+- Clear **professional consultation reminder**
+- Emphasis that threat levels are **estimates**, not definitive diagnoses
+
+### 8.4 Key Technologies
+
+- **Expert Model**: Qwen/Qwen1.5-1.8B (finetuned for Nigerian agriculture)
+- **Gemini Models**: gemini-3-flash-preview (soil), gemini-2.5-flash (disease, voice)
+- **RAG**: LangChain + FAISS + SentenceTransformers
+- **Language Processing**: FastText (detection) + NLLB (translation)
+- **Backend**: FastAPI + Uvicorn + Docker
+- **Deployment**: Hugging Face Spaces (CPU-optimized)
+
+### 8.5 Developer & Credits
+
+**Developer**: Ifeanyi Amogu Shalom  
+**Intended Users**: Farmers, agronomists, agricultural extension officers, and agricultural support workers in Nigeria and similar contexts
+
+---
+
+## 9. Future Improvements & Roadmap
+
+### 9.1 Potential Enhancements
+
+- **Model Fine-tuning**: Further fine-tune Qwen on Nigerian agricultural datasets
+- **Multi-modal RAG**: Integrate images into RAG for visual similarity search
+- **Offline Mode**: Support for offline operation in areas with poor connectivity
+- **Mobile App**: Native mobile applications for better user experience
+- **Expert Network Integration**: Direct connection to network of agronomists/veterinarians
+- **Historical Tracking**: Track disease progression and treatment outcomes over time
+
+### 9.2 Technical Improvements
+
+- **Response Caching**: Cache common queries to reduce latency
+- **Model Quantization**: Further optimize models for CPU inference
+- **Better Error Handling**: More robust error messages and fallback mechanisms
+- **Monitoring & Analytics**: Track system performance and user feedback
+
+---
+
+**Last Updated**: 2026 
+**Version**: 1.0  
+**Status**: Production (Hugging Face Spaces)

app/agents/disease_agent.py

@@ -30,17 +30,22 @@ DISEASE_SYSTEM_PROMPT = """
 You are a multilingual agricultural disease expert fluent in Igbo, Hausa, Yoruba, and English.
 You specialize in identifying and diagnosing plant and animal diseases common in Nigerian and African agriculture.
 
-When analyzing images or voice descriptions:
-1. Identify the disease or condition (if visible/described)
-2. Provide the scientific and common name
-3. Explain symptoms visible in the image or described
-4. Assess severity if possible
-5. Provide treatment recommendations
-6. Suggest preventive measures
-7. Consider local context (Nigerian climate, common crops/livestock)
+When analyzing images or voice descriptions, your answer must be **brief and direct**:
+1. Name the most likely disease (scientific + common name) in 1 short line.
+2. State an overall **Threat level: Low / Moderate / High / Uncertain** (be cautious; only use High when signs are clearly severe or fast‑spreading).
+3. List 2–3 key symptoms you see or infer.
+4. Give 2–3 clear treatment steps (bullets).
+5. Give 1–2 simple prevention tips.
 
-Respond naturally in the language the user uses, or provide translations in all four languages if asked.
-Be clear, practical, and provide actionable advice for farmers.
+Safety:
+- Always remind the farmer to **consult a local agronomist, veterinary doctor, or agricultural extension officer** for confirmation and a full treatment plan.
+- If you are not confident, set threat level to **Uncertain** and say the farmer must see a professional.
+
+Style:
+- Respond naturally in the user's language (or all four languages if asked).
+- Use **short sentences and bullet points**.
+- Avoid long explanations, stories, or extra examples.
+- Focus only on what the farmer needs to do next.
 """
 
 
@@ -88,6 +93,15 @@ def classify_disease_from_image(image_bytes: bytes, image_mime_type: str = "imag
         
         classification_text = response.text if hasattr(response, 'text') else str(response)
         
+        # Always append a professional advice reminder
+        disclaimer = (
+            "\n\nIMPORTANT: This threat level is an estimate based only on the image and description. "
+            "For an accurate diagnosis and treatment plan, please consult a qualified agronomist, "
+            "veterinary doctor, or local agricultural extension officer."
+        )
+        if "IMPORTANT: This threat level is an estimate" not in classification_text:
+            classification_text = classification_text.strip() + disclaimer
+        
         logging.info("Disease classification from image completed successfully")
         
         return {
@@ -146,6 +160,15 @@ def classify_disease_from_text(text_description: str, language: str = "en") -> D
         
         classification_text = response.text if hasattr(response, 'text') else str(response)
         
+        # Always append a professional advice reminder
+        disclaimer = (
+            "\n\nIMPORTANT: This threat level is an estimate based only on your description. "
+            "For an accurate diagnosis and treatment plan, please consult a qualified agronomist, "
+            "veterinary doctor, or local agricultural extension officer."
+        )
+        if "IMPORTANT: This threat level is an estimate" not in classification_text:
+            classification_text = classification_text.strip() + disclaimer
+        
         logging.info("Disease classification from text completed successfully")
         
         return {

app/agents/live_voice_agent.py

@@ -37,11 +37,17 @@ You specialize in:
 3. General farming advice
 4. Weather-related agricultural guidance
 
-When the user speaks to you, respond naturally in the language they used, or provide translations in all four languages if asked.
-You can also see images; if an image is provided, classify it and describe it in the context of agricultural disease detection or farming advice.
+When the user speaks to you:
+- Respond naturally in the language they used (or all four languages if they ask).
+- Keep answers **brief, clear, and straight to the point**.
+- Focus on **what the farmer should do next** (2–4 key steps).
 
-Be clear, practical, and provide actionable advice for farmers.
-Use simple language with occasional emojis to make responses friendly and accessible.
+If an image is provided, classify it and describe it in the context of agricultural disease detection or farming advice, again in a short, direct way.
+
+Style:
+- Use simple language farmers can easily understand.
+- Prefer short sentences and bullet points over long paragraphs.
+- Avoid long stories, unrelated examples, or extra small talk.
 """
 
 

app/agents/soil_agent.py

@@ -26,19 +26,19 @@ except Exception as e:
 
 SOIL_SYSTEM_PROMPT = """
 You are an expert soil scientist and agronomist specializing in Nigerian and African agricultural soils.
-Your role is to analyze soil reports and field data to provide comprehensive, actionable soil analysis.
+Your role is to analyze soil reports and field data to provide **brief, direct, and actionable** soil advice.
 
-When analyzing soil data, consider:
-1. Soil composition (pH, nitrogen, phosphorus, potassium, organic matter, etc.)
-2. Soil texture and structure
-3. Nutrient deficiencies or excesses
-4. Recommendations for crop suitability
-5. Fertilizer recommendations
-6. Soil improvement strategies
-7. Regional context (Nigerian states, climate, typical crops)
+When analyzing soil data, focus on the most important points:
+1. Current soil condition (very short summary)
+2. Key nutrient issues (deficiencies or excesses)
+3. 1–3 best crops for this soil
+4. Clear fertilizer and amendment recommendations
+5. Simple soil improvement steps farmers can act on immediately
 
-Provide clear, practical advice in simple language that farmers can understand.
-Include specific recommendations with quantities where applicable.
+Style:
+- Keep answers **short and to the point** (no long essays).
+- Use simple language farmers can easily understand.
+- Prefer short paragraphs or bullet points over long text.
 """