Update main.py

main.py

@@ -1,14 +1,17 @@
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, UploadFile, File, Form
 from fastapi.responses import JSONResponse
 from PIL import Image
 from transformers import pipeline
 import io, os, re
 import google.generativeai as genai
 
-app = FastAPI(title="🌾 AI Plant Disease Diagnosis Expert")
+app = FastAPI(title="🌾 AI Plant Disease Diagnosis Expert (Multilingual)")
 
 # === Configure Gemini ===
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+if not GOOGLE_API_KEY:
+    raise ValueError("⚠️ Missing GOOGLE_API_KEY environment variable!")
+genai.configure(api_key=GOOGLE_API_KEY)
 
 # === Load Hugging Face model ===
 print("🔍 Loading Hugging Face disease model...")
@@ -27,17 +30,19 @@ def clean_label(label: str):
 
 # === Helper: clean Gemini output ===
 def clean_text(text: str):
-    # Remove markdown and escape sequences for clean JSON response
     text = re.sub(r"[*#`\-]+", "", text)
     text = re.sub(r"\n{2,}", "\n", text)
     return text.strip()
 
 # === Main prediction route ===
 @app.post("/predict")
-async def predict(file: UploadFile = File(...)):
+async def predict(
+    file: UploadFile = File(...),
+    language: str = Form("en")  # Default: English; supports ISO codes like 'hi', 'mr', 'ta', etc.
+):
     """
     Accepts an image of a plant leaf, classifies disease via Hugging Face model,
-    and generates a professional, clean report based on both model prediction and Gemini analysis.
+    and generates a professional, multilingual diagnosis report via Gemini.
     """
     try:
         # 1️⃣ Read uploaded image
@@ -48,7 +53,6 @@ async def predict(file: UploadFile = File(...)):
         hf_results = hf_pipe(image)
         sorted_results = sorted(hf_results, key=lambda x: x["score"], reverse=True)
 
-        # Prepare list of all predictions
         all_predictions = [
             {"label": clean_label(r["label"]), "confidence": round(r["score"], 4)}
             for r in sorted_results
@@ -58,14 +62,28 @@ async def predict(file: UploadFile = File(...)):
         disease = top_pred["label"]
         confidence = top_pred["confidence"]
 
-        # 3️⃣ Generate structured expert-style report using Gemini
+        # 3️⃣ Language-based dynamic prompt for Gemini
+        language_name = {
+            "en": "English",
+            "hi": "Hindi",
+            "mr": "Marathi",
+            "ta": "Tamil",
+            "bn": "Bengali",
+            "te": "Telugu",
+            "gu": "Gujarati",
+            "pa": "Punjabi",
+            "kn": "Kannada",
+            "ml": "Malayalam"
+        }.get(language.lower(), "English")
+
         prompt = f"""
-You are an experienced agricultural plant pathologist.
-You received a clear, close-up photo of a diseased crop leaf.
-The AI disease detection model identified it as '{disease}' with a confidence of {confidence*100:.1f}%.
-Use your own observation of the image and this information to prepare a structured, formal diagnosis report.
+You are an agricultural plant pathologist.
+You are writing a disease diagnosis report for a farmer in {language_name}.
+The detected disease is '{disease}' with {confidence*100:.1f}% confidence.
+Use the photo and this data to write a structured diagnosis.
 
-Your output must follow this structure:
+Write your answer completely in {language_name}.
+Follow this format:
 1. Disease Name
 2. Description (1-2 sentences)
 3. Visible Symptoms (bullet list)
@@ -73,31 +91,35 @@ Your output must follow this structure:
 5. Ideal Conditions for Spread
 6. Safe Management & Control Steps (5-6 points)
 7. Preventive Measures for Future
-8. Verification & Expert Advice (how to confirm or get local help)
-9. Disclaimer (short, professional line that this is an advisory opinion, not a lab test)
+8. Verification & Expert Advice
+9. Disclaimer
 
-Keep the tone confident, clear, and neutral.
-Do NOT mention AI, Gemini, or prediction models.
-Avoid escape symbols like *, -, or ###.
-Use plain text only.
-Keep it concise, but convincing.
-"""
+Keep tone clear and farmer-friendly.
+Avoid mentioning AI or models.
+Use plain text, no markdown or symbols.
+        """
 
+        # 4️⃣ Generate multilingual report using Gemini
         model = genai.GenerativeModel("gemini-2.5-flash")
-        gemini_resp = model.generate_content([prompt, {"mime_type": "image/jpeg", "data": image_bytes}])
-        explanation = clean_text(gemini_resp.text if hasattr(gemini_resp, "text") else str(gemini_resp))
-
-        # 4️⃣ Combine all data neatly
+        gemini_resp = model.generate_content(
+            [prompt, {"mime_type": "image/jpeg", "data": image_bytes}]
+        )
+        explanation = clean_text(
+            gemini_resp.text if hasattr(gemini_resp, "text") else str(gemini_resp)
+        )
+
+        # 5️⃣ Combine and return
         report = {
-            "Report Title": "Plant Disease Diagnostic Summary",
+            "Report Title": "🌾 Plant Disease Diagnostic Summary",
+            "Language": language_name,
             "Disease Name": disease,
             "Model Confidence": f"{confidence*100:.2f}%",
             "Detailed Report": explanation,
             "Other Possible Diseases": all_predictions[1:5],
             "Note": (
                 "This report is for agricultural guidance only. "
-                "Farmers are advised to consult a certified agronomist or local agricultural extension officer "
-                "for laboratory confirmation before applying any treatment."
+                "Farmers are advised to consult a certified agronomist or local agricultural officer "
+                "for laboratory confirmation before applying treatment."
             )
         }