Update app.py

app.py

@@ -6,6 +6,8 @@ import hmac
 import hashlib
 import pandas as pd
 import os
+import re
+import statistics
 from datetime import datetime
 from typing import Optional, Dict, Any, List
 
@@ -14,16 +16,19 @@ app = FastAPI()
 # Your Retell.ai secret key (get from Retell.ai dashboard)
 RETELL_SECRET_KEY = "key_bdb05277a4587c7441bdad4a2c1b"
 
+# --- WEATHER CONFIG ---
+WEATHER_API_KEY = "ee75ffd59875aa5ca6c207e594336b30"
+
 # Load CSV data on startup
 def load_csv_data():
     """Load all CSV files into memory"""
     data = {}
     csv_files = {
         'contact_info': '/app/data/contact_info.csv',
-        'crop_advisory': '/app/data/crop_advisory.csv', 
+        'crop_advisory': '/app/data/crop_advisory.csv',
         'government_schemes': '/app/data/government_schemes.csv'
     }
-    
+
     for key, file_path in csv_files.items():
         try:
             if os.path.exists(file_path):
@@ -39,22 +44,24 @@ def load_csv_data():
         except Exception as e:
             print(f"Error loading {key}: {str(e)}")
             data[key] = pd.DataFrame()
-    
+
     return data
 
 # Load CSV data
 csv_data = load_csv_data()
-WEATHER_API_KEY = "ee75ffd59875aa5ca6c207e594336b30"
 
 def get_weather(city: str):
     """Fetches weather data from OpenWeatherMap API."""
+    if not city:
+        return None, None, None, None
     url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={WEATHER_API_KEY}&units=metric"
     try:
         response = requests.get(url, timeout=5)
         response.raise_for_status()
         data = response.json()
 
-        if data.get("cod") == 200:
+        # OpenWeather returns cod as int or string depending on response
+        if str(data.get("cod")) == "200":
             weather_description = data['weather'][0]['description']
             temperature = data['main']['temp']
             humidity = data['main']['humidity']
@@ -83,39 +90,192 @@ def search_csv_data(df: pd.DataFrame, search_terms: Dict[str, str]) -> pd.DataFr
     """Search dataframe based on multiple criteria"""
     if df.empty:
         return df
-    
+
     result = df.copy()
     for column, value in search_terms.items():
         if column in df.columns and value:
             # Case-insensitive partial matching
             result = result[result[column].astype(str).str.contains(value, case=False, na=False)]
-    
+
     return result
 
+# -------------------------
+# Helper utilities
+# -------------------------
+def find_column(df: pd.DataFrame, candidates: List[str]) -> Optional[str]:
+    """Return first matching column name from candidates (case-insensitive) or None."""
+    cols = {c.lower(): c for c in df.columns}
+    for cand in candidates:
+        if cand.lower() in cols:
+            return cols[cand.lower()]
+    return None
+
+def extract_number_from_price(val: Any) -> Optional[float]:
+    """
+    Try to extract numeric value from price strings like "₹2,180 per quintal" or "2180".
+    Returns float or None if not parseable.
+    """
+    if pd.isna(val):
+        return None
+    if isinstance(val, (int, float)):
+        return float(val)
+    s = str(val)
+    # remove currency symbols and non-digit characters except dot and minus
+    # first try to find first numeric group
+    # remove common words like per, quintal
+    # Use regex to capture numbers like 2,180.50 or 2180.5
+    match = re.search(r"(-?\d{1,3}(?:[,]\d{3})*(?:\.\d+)?|-?\d+(?:\.\d+)?)", s.replace('₹','').replace('Rs','').replace('INR',''))
+    if match:
+        num = match.group(0).replace(',', '')
+        try:
+            return float(num)
+        except:
+            return None
+    return None
+
+def format_scheme_row(row: pd.Series, mapping: Dict[str,str]) -> Dict[str,str]:
+    """Build a consistent scheme dict from a CSV row using mapping of fields to column names."""
+    return {
+        "scheme": row.get(mapping.get("name", ""), "N/A"),
+        "introduction": row.get(mapping.get("introduction", ""), ""),
+        "objective": row.get(mapping.get("objective", ""), ""),
+        "benefit": row.get(mapping.get("benefit", ""), ""),
+        "eligibility": row.get(mapping.get("eligibility", ""), ""),
+        "process": row.get(mapping.get("process", ""), "Contact local agriculture office"),
+        "contact": row.get(mapping.get("contact", ""), ""),
+        "extra": row.get(mapping.get("extra", ""), ""),
+    }
+
+def get_schemes_from_csv(farmer_category: str, land_size: float, state: str, crop_type: str) -> List[Dict[str,str]]:
+    """
+    Read government_schemes dataframe and return a list of scheme dicts.
+    This function attempts to surface the most relevant schemes first but will
+    return all schemes if filtering doesn't match.
+    """
+    schemes_out = []
+    df = csv_data.get('government_schemes', pd.DataFrame())
+    if df.empty:
+        return []
+
+    # build mapping for column names (supports different CSV header variants)
+    mapping = {
+        "name": find_column(df, ["Name", "scheme_name", "Scheme", "Scheme Name"]),
+        "introduction": find_column(df, ["Introduction", "introduction", "Description"]),
+        "objective": find_column(df, ["Objective", "objective"]),
+        "benefit": find_column(df, ["Benefits", "Benefit", "benefit"]),
+        "eligibility": find_column(df, ["Eligibility Criteria", "eligibility", "Eligibility", "eligibility_criteria"]),
+        "process": find_column(df, ["Application Process & Required Documents", "application_process", "Process", "application_process & required_documents"]),
+        "contact": find_column(df, ["Helpline & Website", "contact", "Helpline", "helpline"]),
+        "extra": find_column(df, ["Extra Details", "extra_details", "Extra"])
+    }
+
+    # Build list of all schemes with formatting
+    all_schemes = []
+    for _, r in df.iterrows():
+        all_schemes.append(format_scheme_row(r, mapping))
+
+    # Try to filter schemes based on simple heuristics:
+    prioritized = []
+    others = []
+
+    state_lower = state.lower() if state else ""
+    farmer_cat_lower = farmer_category.lower() if farmer_category else ""
+    crop_lower = crop_type.lower() if crop_type else ""
+
+    for s in all_schemes:
+        elig = str(s.get("eligibility", "")).lower()
+        text_blob = " ".join([
+            str(s.get("scheme","") or ""),
+            str(s.get("introduction","") or ""),
+            str(s.get("objective","") or ""),
+            str(s.get("benefit","") or ""),
+            str(s.get("eligibility","") or ""),
+            str(s.get("extra","") or "")
+        ]).lower()
+
+        score = 0
+        # If scheme mentions the state explicitly -> higher relevance
+        if state_lower and state_lower in text_blob:
+            score += 2
+        # If eligibility explicitly mentions landholding and user has land_size > 0
+        if land_size and ("land" in elig or "landholding" in elig or "land" in text_blob):
+            score += 2
+        # If eligibility says "all farmers" or similar, raise modestly
+        if "all" in elig or "all farmers" in elig or "all landholding" in elig:
+            score += 1
+        # crop-specific mention
+        if crop_lower and crop_lower in text_blob:
+            score += 2
+        # farmer category mention
+        if farmer_cat_lower and farmer_cat_lower in text_blob:
+            score += 1
+
+        # Put high-scored into prioritized list
+        if score >= 2:
+            prioritized.append((score, s))
+        else:
+            others.append((score, s))
+
+    # sort priority by score desc
+    prioritized.sort(key=lambda x: x[0], reverse=True)
+    others.sort(key=lambda x: x[0], reverse=True)
+
+    # return only scheme dicts, prioritized first
+    schemes_out = [s for _, s in prioritized] + [s for _, s in others]
+    return schemes_out
+
+# -------------------------
+# End helpers
+# -------------------------
+
 @app.post("/api/market-prices")
 async def market_prices(request: dict):
+    # Keep your request shape usage intact
     crop_name = request.get("query", {}).get("crop_name", "").strip()
     state = request.get("query", {}).get("state", "").strip()
     district = request.get("query", {}).get("district", "").strip()
 
-    if "crop_advisory" in csv_data:
-        df = csv_data["crop_advisory"]
-        mask = (
-            df["crop_name"].str.lower() == crop_name.lower()
-        ) & (df["state"].str.lower() == state.lower()) & (
-            df["district"].str.lower() == district.lower()
-        )
+    # Safely handle missing CSV or missing expected columns
+    if "crop_advisory" in csv_data and not csv_data["crop_advisory"].empty:
+        df = csv_data["crop_advisory"].copy()
+
+        # find likely column names for crop, state, district, price
+        crop_col = find_column(df, ["crop_name", "crop", "Crop", "Crop Name"])
+        state_col = find_column(df, ["state", "State", "state_name"])
+        district_col = find_column(df, ["district", "District", "district_name"])
+        price_col = find_column(df, ["price", "Price", "market_price", "market price", "price_per_quintal"])
+
+        # build mask progressively (use contains if exact match column not present)
+        mask = pd.Series([True] * len(df))
+        if crop_col and crop_name:
+            mask = mask & df[crop_col].astype(str).str.contains(crop_name, case=False, na=False)
+        if state_col and state:
+            mask = mask & df[state_col].astype(str).str.contains(state, case=False, na=False)
+        if district_col and district:
+            mask = mask & df[district_col].astype(str).str.contains(district, case=False, na=False)
+
         matches = df[mask]
 
         if not matches.empty:
-            try:
-                avg_price = matches["price"].astype(float).mean()
-                result = (
-                    f"The average market price of {crop_name} in {district}, {state} "
-                    f"is ₹{avg_price:.2f} per quintal."
-                )
-            except Exception:
-                result = f"Market data found but price column not numeric for {crop_name} in {district}, {state}."
+            # compute average over numeric-parsable values in price_col if exists
+            avg_price = None
+            parsed_prices = []
+            if price_col:
+                for v in matches[price_col].tolist():
+                    num = extract_number_from_price(v)
+                    if num is not None:
+                        parsed_prices.append(num)
+                if parsed_prices:
+                    try:
+                        avg_price = statistics.mean(parsed_prices)
+                    except Exception:
+                        avg_price = None
+
+            if avg_price is not None:
+                result = f"The average market price of {crop_name} in {district}, {state} is ₹{avg_price:.2f} per quintal."
+            else:
+                # If price_col absent or non-numeric, fallback to your previous text but mention CSV found
+                result = f"Market data found for {crop_name} in {district}, {state} but numeric price values were not available."
 
             return {
                 "success": True,
@@ -123,12 +283,13 @@ async def market_prices(request: dict):
                 "data": matches.to_dict(orient="records")
             }
 
+    # fallback to previous mock behavior (keeps your logic)
     return {
         "success": False,
         "result": f"No market price data found for {crop_name} in {district}, {state}."
     }
 
-@app.post("/api/scheme-eligibility") 
+@app.post("/api/scheme-eligibility")
 async def scheme_eligibility_endpoint(
     request: Request,
     x_retell_signature: str = Header(None, alias="X-Retell-Signature")
@@ -136,75 +297,71 @@ async def scheme_eligibility_endpoint(
     """Handle scheme eligibility function call from Retell.ai"""
     request_body = await request.body()
     retell_request = json.loads(request_body.decode('utf-8'))
-    
+
     # Extract arguments
     farmer_category = retell_request["args"].get("farmer_category", "")
     land_size = retell_request["args"].get("land_size", 0)
     state = retell_request["args"].get("state", "")
     crop_type = retell_request["args"].get("crop_type", "")
-    
+
     try:
         eligible_schemes = []
-        
-        # Search government schemes CSV
+
+        # Search government schemes CSV and apply simple relevance heuristics
         if not csv_data['government_schemes'].empty:
-            # Get all schemes first, then filter if needed
-            scheme_data = csv_data['government_schemes'].copy()
-            
-            for _, scheme in scheme_data.iterrows():
-                eligible_schemes.append({
-                    "scheme": scheme.get('scheme_name', 'N/A'),
-                    "benefit": scheme.get('benefit', 'N/A'),
-                    "description": scheme.get('description', ''),
-                    "eligibility": scheme.get('eligibility', ''),
-                    "process": scheme.get('application_process', 'Contact local agriculture office'),
-                    "contact": scheme.get('contact', '')
-                })
-        
-        # Add default schemes if no CSV data
+            eligible_schemes = get_schemes_from_csv(farmer_category, land_size, state, crop_type)
+
+        # Add default schemes if no CSV data or as fallback
         if not eligible_schemes:
             # PM-KISAN eligibility
-            if land_size > 0:
+            if land_size and float(land_size) > 0:
                 eligible_schemes.append({
                     "scheme": "PM-KISAN",
                     "benefit": "₹6,000 per year in 3 installments",
-                    "process": "Apply online at pmkisan.gov.in or visit nearest CSC"
+                    "description": "Direct income support to landholding farmer families.",
+                    "eligibility": "All landholding farmer families.",
+                    "process": "Apply online at pmkisan.gov.in or visit nearest CSC",
+                    "contact": "https://pmkisan.gov.in/"
                 })
-            
+
             # Crop Insurance
             eligible_schemes.append({
                 "scheme": "Pradhan Mantri Fasal Bima Yojana",
                 "benefit": "Comprehensive crop insurance coverage",
-                "process": "Contact your nearest bank or insurance agent"
+                "description": "Crop insurance against natural calamities, pests, and diseases.",
+                "eligibility": "All farmers in notified crops/areas",
+                "process": "Contact your nearest bank, CSC or PMFBY portal",
+                "contact": "https://pmfby.gov.in/"
             })
-            
+
             # State-specific schemes
-            if state.lower() == "punjab":
+            if state and state.lower() == "punjab":
                 eligible_schemes.append({
                     "scheme": "Punjab Crop Diversification Scheme",
                     "benefit": "₹17,500 per hectare for diversification",
-                    "process": "Contact District Agriculture Officer"
+                    "process": "Contact District Agriculture Officer",
+                    "contact": ""
                 })
-        
-        # Format response for voice
+
+        # Format response for voice (limit to first 3 items, keep original style)
         if eligible_schemes:
             schemes_text = f"You are eligible for {len(eligible_schemes)} government schemes: "
             for i, scheme in enumerate(eligible_schemes[:3]):  # Limit to first 3 for voice response
-                contact_info = f" Apply through {scheme['process']}" if scheme['process'] else ""
+                contact_info = f" Apply through {scheme.get('process','Contact local agriculture office')}" if scheme.get('process') else ""
                 if scheme.get('contact'):
-                    contact_info += f" or contact {scheme['contact']}"
-                schemes_text += f"{i+1}. {scheme['scheme']} - {scheme['benefit']}.{contact_info}. "
-            
+                    contact_info += f" or contact {scheme.get('contact')}"
+                schemes_text += f"{i+1}. {scheme.get('scheme','N/A')} - {scheme.get('benefit', scheme.get('description','N/A'))}.{contact_info}. "
+
             if len(eligible_schemes) > 3:
                 schemes_text += f"And {len(eligible_schemes)-3} more schemes available."
         else:
             schemes_text = "I couldn't find specific schemes for your profile. Please contact your local agriculture department for personalized advice."
-        
+
         return {
             "result": schemes_text,
             "eligible_schemes": eligible_schemes
         }
-        
+
     except Exception as e:
         return {
             "result": "I'm having trouble accessing scheme information right now. Please contact your local agriculture officer or visit the nearest CSC for scheme details.",
@@ -213,6 +370,7 @@ async def scheme_eligibility_endpoint(
 
 @app.post("/api/weather-advisory")
 async def weather_advisory(request: dict):
+    # Keep your request shape usage intact
     city = request.get("query", {}).get("location", "").strip()
 
     temperature, humidity, description, pressure = get_weather(city)
@@ -252,22 +410,22 @@ async def weather_advisory(request: dict):
 
 @app.post("/api/crop-advisory")
 async def crop_advisory_endpoint(
-    request: Request, 
+    request: Request,
     x_retell_signature: str = Header(None, alias="X-Retell-Signature")
 ):
     """Handle crop advisory function call from Retell.ai"""
     request_body = await request.body()
     retell_request = json.loads(request_body.decode('utf-8'))
-    
+
     crop_name = retell_request["args"].get("crop_name", "")
     growth_stage = retell_request["args"].get("growth_stage", "")
     issue_type = retell_request["args"].get("issue_type", "general")
     state = retell_request["args"].get("state", "")
-    
+
     try:
         advisory = None
         contact_info = ""
-        
+
         # Search crop advisory CSV
         if not csv_data['crop_advisory'].empty:
             search_terms = {}
@@ -276,13 +434,13 @@ async def crop_advisory_endpoint(
                 crop_matches = csv_data['crop_advisory'][
                     csv_data['crop_advisory']['crop'].str.contains(crop_name, case=False, na=False)
                 ]
-                
+
                 if not crop_matches.empty:
                     crop_info = crop_matches.iloc[0]
-                    
+
                     # Build advisory based on available data
                     advisory_parts = []
-                    
+
                     if issue_type == "general":
                         if pd.notna(crop_info.get('sowing_time')):
                             advisory_parts.append(f"Sowing time: {crop_info['sowing_time']}")
@@ -290,38 +448,38 @@ async def crop_advisory_endpoint(
                             advisory_parts.append(f"Recommended fertilizer: {crop_info['fertilizer']}")
                         if pd.notna(crop_info.get('season')):
                             advisory_parts.append(f"Best season: {crop_info['season']}")
-                    
+
                     # Check for specific issues
                     if pd.notna(crop_info.get('common_issues')) and pd.notna(crop_info.get('solution')):
                         if issue_type in ['pest', 'disease'] or issue_type == 'general':
                             advisory_parts.append(f"For {crop_info['common_issues']}: {crop_info['solution']}")
-                    
+
                     if advisory_parts:
                         advisory = f"For {crop_name}: " + ". ".join(advisory_parts)
-        
+
         # Search contact info CSV  
         if not csv_data['contact_info'].empty and state:
             # Search by state
             contact_matches = csv_data['contact_info'][
                 csv_data['contact_info']['state'].str.contains(state, case=False, na=False)
             ]
-            
+
             if not contact_matches.empty:
                 contact_match = contact_matches.iloc[0]
                 contact_parts = []
-                
+
                 if pd.notna(contact_match.get('agriculture_officer')):
                     contact_parts.append(f"Agriculture Officer at {contact_match['agriculture_officer']}")
-                
+
                 if pd.notna(contact_match.get('kvk_contact')):
                     contact_parts.append(f"KVK at {contact_match['kvk_contact']}")
-                
+
                 if pd.notna(contact_match.get('kisan_call_center')):
                     contact_parts.append(f"Kisan Call Center at {contact_match['kisan_call_center']}")
-                
+
                 if contact_parts:
                     contact_info = f"For detailed advice in {state}, contact: " + " or ".join(contact_parts) + "."
-        
+
         # Fallback advisory
         if not advisory:
             if crop_name.lower() == "wheat" and issue_type == "pest":
@@ -330,18 +488,18 @@ async def crop_advisory_endpoint(
                 advisory = "For rice disease management: If you see brown spots on leaves, it might be blast disease. Apply Tricyclazole 75% WP at 0.6g per liter. Ensure proper drainage."
             else:
                 advisory = f"For {crop_name} at {growth_stage} stage: Monitor crop regularly, maintain proper spacing, apply fertilizers as per soil test recommendations."
-        
+
         if not contact_info:
             contact_info = f"For detailed advice, contact your local Krishi Vigyan Kendra or Agriculture Officer in {state}. You can also call the Kisan Call Centre at 1800-1801-551."
-        
+
         result_text = f"{advisory} {contact_info}"
-        
+
         return {
             "result": result_text,
             "recommendations": advisory,
             "contact_info": contact_info
         }
-        
+
     except Exception as e:
         return {
             "result": f"I couldn't provide specific advice for {crop_name} right now. Please contact your local agriculture extension officer for crop-specific guidance.",
@@ -364,7 +522,7 @@ async def csv_status():
 @app.get("/health")
 async def health_check():
     return {
-        "status": "healthy", 
+        "status": "healthy",
         "service": "Krishi Mitra API",
         "csv_files_loaded": {key: len(df) for key, df in csv_data.items()}
     }
@@ -375,7 +533,7 @@ async def root():
         "message": "Krishi Mitra API is running!",
         "endpoints": [
             "/api/market-prices",
-            "/api/scheme-eligibility", 
+            "/api/scheme-eligibility",
             "/api/weather-advisory",
             "/api/crop-advisory",
             "/api/csv-status",
@@ -385,4 +543,4 @@ async def root():
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)