KrishiMitraAI — Kerala Farmers' AI-Powered Agricultural Advisor

An end-to-end Streamlit application that provides Kerala-specific agricultural advice using a hybrid RAG (Retrieval-Augmented Generation) system, seasonal crop calendar, district-wise advisory, weather-based guidance, and optional voice features. Supports Malayalam and English.

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Highlights

• Chat with an AI advisor grounded in Kerala Agricultural Development Policy (PDF) and a curated FAQ dataset (CSV)
• FAISS vector search with multilingual sentence-transformer embeddings and keyword fallback index
• Optional local LLM via Ollama; default cloud LLM via Gemini
• Crop disease image analysis using Gemini Vision
• Seasonal calendar with month-wise crops, activities, and tips
• District-wise advisory: crops, soils, schemes, and recommendations
• Weather advisory with mock Kerala patterns and actionable guidance
• Voice input/output using browser Web Speech API (no backend dependency)

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Project Structure

KrishiMitraAI/
├─ app.py                      # Streamlit UI and app entrypoint
├─ rag_system.py               # RAG pipeline (FAISS + Gemini/Ollama) and image analysis
├─ document_processor.py       # PDF/CSV ingestion and chunking
├─ utils.py                    # Language detection, text cleaning/formatting, fallbacks
├─ crop_calendar.py            # Kerala agricultural calendar (month-wise)
├─ kerala_districts.py         # District-wise agri info and tips
├─ weather_integration.py      # Weather-based advisory (mock data)
├─ voice_assistant.py          # Web Speech API integrations for voice I/O
├─ attached_assets/
│  ├─ Agri-Dev-Policy_*.pdf    # Kerala policy PDF (knowledge source)
│  ├─ Farming_FAQ_*.csv        # FAQ dataset (knowledge source)
│  ├─ questionsv4.csv          # Optional additional Q&A
│  └─ index/                   # FAISS index files (auto-built/loaded)
├─ pyproject.toml              # Dependencies (uv/pip compatible)
└─ uv.lock                     # Lockfile (optional)

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Prerequisites

• Python 3.11+
• Windows, macOS, or Linux
• Internet access for first-time model downloads and Gemini API (unless using Ollama)
• Optional: GPU with CUDA for faster FAISS (auto-detected)

API Keys

• Gemini: set GEMINI_API_KEY in your environment.
  • The code currently contains a placeholder; for production, always use an environment variable.

Optional: Ollama (Local LLM)

• Install Ollama from https://ollama.com
• Pull a compatible model (configured default is llama2-uncensored:7b):

ollama pull llama2-uncensored:7b

The app auto-detects Ollama; if not found, it falls back to Gemini.

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Setup

1) Clone and enter the project

git clone <your-repo-url>
cd KrishiMitraAI

2) Create and activate a virtual environment

• Windows (PowerShell):

python -m venv .venv
. .venv\Scripts\Activate.ps1

• macOS/Linux (bash):

python3 -m venv .venv
source .venv/bin/activate

3) Install dependencies

Using pip:

pip install -U pip
pip install -r requirements.txt  # if you create one
# or install via pyproject
pip install .

Or using uv (fast, recommended):

pip install uv
uv sync

4) Add data assets

Ensure the following exist under attached_assets/:

• Agri-Dev-Policy_*.pdf (provided)
• Farming_FAQ_Assistant_Dataset_*.csv (provided)
• Optional: questionsv4.csv

5) Set environment variables

• Windows (PowerShell):

$env:GEMINI_API_KEY="YOUR_KEY"

• macOS/Linux:

export GEMINI_API_KEY="YOUR_KEY"

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Run

Start the Streamlit app:

streamlit run app.py

Then open the provided local URL in your browser.

First launch builds/loads the FAISS index and keyword index in background threads. The sidebar shows build status, device (CPU/GPU), and knowledge base stats.

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Usage Guide

• Chat Tab: Ask questions in Malayalam or English. Answers are grounded in the PDF/CSV knowledge base.
• Crop Disease Detection: Upload a crop image and click “Analyze Crop” to get identification and treatment suggestions.
• Seasonal Calendar: Choose a month to see recommended crops, maintenance, irrigation, fertilizer, and disease watch.
• District Advisory: Pick your district (and optionally a crop) to get localized guidance and schemes.
• Weather Advisory: Select a location to view mock real-time weather and actionable recommendations; shows a 3‑day outlook.
• Voice: Expand Voice Input to speak queries; responses can be played via “Listen to Response.”

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Architecture

flowchart LR
  subgraph UI[Streamlit UI]
    A[Chat UI]
    B[Image Upload]
    C[Calendar]
    D[District Advisory]
    E[Weather Advisory]
    V[Voice I/O]
  end

  A -- query --> RAG
  B -- image --> IMG[Gemini Vision]
  C --> CAL[KeralaAgriculturalCalendar]
  D --> DIST[KeralaDistrictAdvisory]
  E --> WTH[WeatherAdvisory]
  V --> A

  subgraph Core
    RAG[KrishiMitraRAG]
    DP[DocumentProcessor]
    VS[(FAISS Index)]
    KW[(Keyword Index)]
  end

  RAG <--chunks--> VS
  RAG <--tokens--> KW
  RAG <--docs--> DP

  subgraph External
    GEM[Gemini API]
    OLL[Ollama (optional)]
  end

  RAG <--> GEM
  RAG <--> OLL

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RAG Flow

sequenceDiagram
  participant User
  participant UI as Streamlit UI
  participant RAG as KrishiMitraRAG
  participant VS as FAISS
  participant KW as Keyword Index
  participant LLM as Gemini/Ollama

  User->>UI: Enter question (ML/EN)
  UI->>RAG: get_response(question)
  RAG->>VS: similarity_search(k=8) [if ready]
  alt Vector store unavailable
    RAG->>KW: keyword search + scoring
  end
  RAG->>RAG: prepare_context + extract_facts(FAQ)
  RAG->>LLM: system+user prompt (language-aware)
  LLM-->>RAG: response text
  alt Empty/weak response
    RAG->>RAG: compose_answer_from_facts
  end
  RAG-->>UI: formatted answer
  UI-->>User: Display answer + optional TTS

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Streamlit UI Flow

flowchart TB
  Start[App Start] --> Init[Initialize RAG + build/load indexes]
  Init --> Sidebar[Sidebar shows status and KB stats]
  Sidebar --> Tabs{Select Tab}
  Tabs -->|Chat| Chat
  Tabs -->|Disease| Disease
  Tabs -->|Calendar| Calendar
  Tabs -->|District| District
  Tabs -->|Weather| Weather
  Chat --> Answer[Display answer + TTS]
  Disease --> Analyze[Gemini Vision analysis]
  Calendar --> MonthAdvice[Monthly advice]
  District --> DistrictAdvice[District recommendations]
  Weather --> WeatherAdvice[Mock data + guidance]

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Configuration Notes

• Embeddings: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 loaded on CPU or CUDA when available.
• Index directory: attached_assets/index/ (auto-created). Safe to delete; will rebuild on next run.
• Language detection: simple Malayalam/English heuristic in utils.detect_language.
• Voice features: implemented via Web Speech API in voice_assistant.py; no server-side speech libs required.

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Troubleshooting

• Vector store building forever:
  • Ensure attached_assets files exist and readable.
  • Delete attached_assets/index/ and restart to rebuild.
• GPU not used:
  • Requires CUDA and torch.cuda.is_available(); otherwise runs on CPU.
• Gemini errors:
  • Verify GEMINI_API_KEY is set and valid.
  • Network/firewall may block API; check connectivity.
• Ollama not used:
  • Confirm ollama list shows the model. App will fall back to Gemini if unavailable.
• Image analysis fails:
  • Ensure JPEG/PNG is clear and under typical browser upload limits.

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Security & Privacy

• Do not commit real API keys. Use environment variables.
• Uploaded images are temporarily saved for processing and then deleted.
• Answers are advisory only; for official guidance, consult local Krishi Bhavan.

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Development

• Run in dev mode and iterate on Streamlit components.
• Key modules to tweak:
  • rag_system.py: retrieval configs, prompts, and LLM provider.
  • document_processor.py: chunk sizes and metadata handling.
  • app.py: UI layout and interactions.

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License

Specify your license here.