Deploy from GitHub: 0df40e607abe71d679ca5b9cb8c5743bae597100

app/config.py

@@ -6,6 +6,8 @@ class Settings(BaseSettings):
     SERPER_API_KEY: str = ""
     SARVAM_API_KEY: str = ""
     OPENAI_API_KEY: str = ""
+    GEMINI_API_KEY: str = ""
     BEARER_TOKEN: str = ""
+    API_ENDPOINT: str = ""
 
 settings = Settings()

app/services/llm_service.py

@@ -1,6 +1,7 @@
 import json
 import base64
 import re
+import traceback
 from app.config import settings
 
 try:
@@ -20,6 +21,12 @@ def _clean_fenced_json(text: str) -> str:
     return response_text.strip()
 
 
+# ---------------------------------------------------------------------------
+# Client helpers
+# ---------------------------------------------------------------------------
+
+GEMINI_MODEL = "gemini-3.5-flash-lite"
+
 def _get_openai_client():
     if not settings.OPENAI_API_KEY:
         raise ValueError("OPENAI_API_KEY is required")
@@ -28,6 +35,22 @@ def _get_openai_client():
     return OpenAI(api_key=settings.OPENAI_API_KEY)
 
 
+def _get_gemini_client():
+    """Return an OpenAI client pointed at Gemini's OpenAI-compatible endpoint."""
+    if OpenAI is None:
+        raise ValueError("openai package is not installed.")
+    if not settings.GEMINI_API_KEY:
+        raise ValueError("GEMINI_API_KEY is required")
+    return OpenAI(
+        api_key=settings.GEMINI_API_KEY,
+        base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
+    )
+
+
+# ---------------------------------------------------------------------------
+# System prompts
+# ---------------------------------------------------------------------------
+
 CHATBOT_SYSTEM_PROMPTS = {
     "en": (
         "You are the AgriVision Chatbot: a friendly, practical agricultural expert for farmers. "
@@ -83,6 +106,55 @@ CHATBOT_SYSTEM_PROMPTS = {
     ),
 }
 
+
+# ---------------------------------------------------------------------------
+# Gemini helpers (via OpenAI-compatible endpoint)
+# ---------------------------------------------------------------------------
+
+def _gemini_text(prompt: str, system_instruction: str = None, temperature: float = 0.4) -> str:
+    """Call Gemini with a text-only prompt via OpenAI-compatible API."""
+    client = _get_gemini_client()
+    messages = []
+    if system_instruction:
+        messages.append({"role": "system", "content": system_instruction})
+    messages.append({"role": "user", "content": prompt})
+
+    completion = client.chat.completions.create(
+        model=GEMINI_MODEL,
+        temperature=temperature,
+        messages=messages,
+    )
+    return completion.choices[0].message.content or ""
+
+
+def _gemini_vision(prompt: str, image_data: bytes, system_instruction: str = None, temperature: float = 0.4) -> str:
+    """Call Gemini with an image + text prompt via OpenAI-compatible API."""
+    client = _get_gemini_client()
+    img_b64 = base64.b64encode(image_data).decode("utf-8")
+
+    messages = []
+    if system_instruction:
+        messages.append({"role": "system", "content": system_instruction})
+    messages.append({
+        "role": "user",
+        "content": [
+            {"type": "text", "text": prompt},
+            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}},
+        ],
+    })
+
+    completion = client.chat.completions.create(
+        model=GEMINI_MODEL,
+        temperature=temperature,
+        messages=messages,
+    )
+    return completion.choices[0].message.content or ""
+
+
+# ---------------------------------------------------------------------------
+# Disease detection
+# ---------------------------------------------------------------------------
+
 def detect_disease(image_data: str, query: str = ""):
     prompt = (
         "You are an expert agricultural plant-pathology assistant for farmers. Analyze this crop image and provide practical advice. "
@@ -114,6 +186,19 @@ def detect_disease(image_data: str, query: str = ""):
         f"\n\nFarmer query/context: {query}"
         "\n\nRespond only in Markdown and keep language simple for farmers."
     )
+
+    # --- Gemini (primary) ---
+    try:
+        print("[LLM] detect_disease: trying Gemini...")
+        result = _gemini_vision(prompt, image_data, temperature=0.4)
+        print("[LLM] detect_disease: Gemini succeeded")
+        return result
+    except Exception as e:
+        print(f"[LLM] detect_disease: Gemini FAILED: {e}")
+        traceback.print_exc()
+        print("[LLM] detect_disease: falling back to OpenAI...")
+
+    # --- OpenAI (fallback) ---
     oai = _get_openai_client()
     img_b64 = base64.b64encode(image_data).decode("utf-8")
     completion = oai.chat.completions.create(
@@ -132,6 +217,10 @@ def detect_disease(image_data: str, query: str = ""):
     return completion.choices[0].message.content or ""
 
 
+# ---------------------------------------------------------------------------
+# Product extraction
+# ---------------------------------------------------------------------------
+
 def extract_agri_products(detection_text: str):
     def _heuristic_product_candidates(text: str):
         # Fallback parser from chemical solution sections when model extraction is empty/invalid.
@@ -145,7 +234,7 @@ def extract_agri_products(detection_text: str):
         ]
 
         for line in (text or "").splitlines():
-            clean = line.strip(" -•*\t")
+            clean = line.strip(" -*\t")
             if not clean:
                 continue
 
@@ -176,6 +265,24 @@ def extract_agri_products(detection_text: str):
         f"Analysis: {detection_text}"
     )
 
+    # --- Gemini (primary) ---
+    try:
+        print("[LLM] extract_agri_products: trying Gemini...")
+        extraction_text = _gemini_text(prompt, temperature=0)
+        extraction_text = _clean_fenced_json(extraction_text)
+        parsed = json.loads(extraction_text)
+        products = parsed.get("products", []) if isinstance(parsed, dict) else []
+        if isinstance(products, list) and products:
+            print("[LLM] extract_agri_products: Gemini succeeded")
+            return products
+        print("[LLM] extract_agri_products: Gemini returned empty, using heuristic")
+        return _heuristic_product_candidates(detection_text)
+    except Exception as e:
+        print(f"[LLM] extract_agri_products: Gemini FAILED: {e}")
+        traceback.print_exc()
+        print("[LLM] extract_agri_products: falling back to OpenAI...")
+
+    # --- OpenAI (fallback) ---
     oai = _get_openai_client()
     completion = oai.chat.completions.create(
         model="gpt-4o-mini",
@@ -195,9 +302,29 @@ def extract_agri_products(detection_text: str):
     except json.JSONDecodeError:
         return _heuristic_product_candidates(detection_text)
 
+
+# ---------------------------------------------------------------------------
+# Chatbot
+# ---------------------------------------------------------------------------
+
 def chat_bot(query: str, image_data: bytes = None, language: str = "en"):
     system_instruction = CHATBOT_SYSTEM_PROMPTS.get(language, CHATBOT_SYSTEM_PROMPTS["en"])
 
+    # --- Gemini (primary) ---
+    try:
+        print("[LLM] chat_bot: trying Gemini...")
+        if image_data:
+            result = _gemini_vision(query, image_data, system_instruction=system_instruction, temperature=0.7)
+        else:
+            result = _gemini_text(query, system_instruction=system_instruction, temperature=0.7)
+        print("[LLM] chat_bot: Gemini succeeded")
+        return result
+    except Exception as e:
+        print(f"[LLM] chat_bot: Gemini FAILED: {e}")
+        traceback.print_exc()
+        print("[LLM] chat_bot: falling back to OpenAI...")
+
+    # --- OpenAI (fallback) ---
     oai = _get_openai_client()
     content = [{"type": "text", "text": query}]
     if image_data:

requirements.txt

@@ -6,3 +6,4 @@ requests
 sarvamai
 python-dotenv
 openai
+google-generativeai

test_gemini.py

@@ -0,0 +1,42 @@
+"""Compare Gemini API speed: OpenAI-compat vs direct REST."""
+import time
+import requests
+import os
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv()
+API_KEY = os.getenv("GEMINI_API_KEY")
+MODEL = "gemini-3.5-flash-lite"
+PROMPT = "What is the capital of France? Reply in one sentence."
+
+# --- Method 1: Direct REST API ---
+print("=== Method 1: Direct REST API ===")
+t1 = time.time()
+url = f"https://generativelanguage.googleapis.com/v1beta/models/{MODEL}:generateContent?key={API_KEY}"
+payload = {"contents": [{"parts": [{"text": PROMPT}]}]}
+resp = requests.post(url, json=payload, timeout=30)
+data = resp.json()
+print(f"Raw response: {data}")
+if "candidates" in data:
+    text = data["candidates"][0]["content"]["parts"][0]["text"]
+    print(f"Response: {text}")
+else:
+    print(f"ERROR: {data.get('error', data)}")
+elapsed1 = time.time() - t1
+print(f"Time: {elapsed1:.1f}s\n")
+
+# --- Method 2: OpenAI-compatible endpoint ---
+print("=== Method 2: OpenAI-compatible endpoint ===")
+t2 = time.time()
+client = OpenAI(
+    api_key=API_KEY,
+    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
+)
+response = client.chat.completions.create(
+    model=MODEL,
+    messages=[{"role": "user", "content": PROMPT}],
+)
+elapsed2 = time.time() - t2
+print(f"Response: {response.choices[0].message.content}")
+print(f"Time: {elapsed2:.1f}s")