CV brand detector +query

backend/src/gemini/__init__.py

@@ -1,6 +1,7 @@
 import os
 from google import genai
 from src.chroma.vector_store import search_documents
+from src.rag.embeddings import get_embedding
 
 GREENWASHING_ANALYSIS_PROMPT = """
 You are an expert Environmental, Social, and Governance (ESG) Analyst specialized in detecting 'Greenwashing'.
@@ -22,15 +23,22 @@ Based on the context provided, give a final verdict:
 
 def ask(prompt):
     client = genai.Client(api_key=os.environ.get("GOOGLE_API_KEY"))
-    return client.models.generate_content(model="gemini-2.0-flash-exp", contents=prompt).text
+    return client.models.generate_content(model="gemini-2.0-flash", contents=prompt).text
 
 def analyze(query, query_embedding, num_results=5, num_alternatives=3):
-    results = search_documents(query_embedding, num_results=num_results + num_alternatives + 5)
+    try:
+        results = search_documents(query_embedding, num_results=num_results + num_alternatives + 5)
+    except Exception as e:
+        print(f"Chroma error: {e}")
+        results = []
     
-    context = "--- START OF REPORT CONTEXT ---\n"
+    if not results:
-    for res in results[:num_results]:
+        context = "No data found in database for this brand."
-        context += f"RELEVANT DATA CHUNK: {res['text']}\n\n"
+    else:
-    context += "--- END OF REPORT CONTEXT ---\n"
+        context = "--- START OF REPORT CONTEXT ---\n"
+        for res in results[:num_results]:
+            context += f"RELEVANT DATA CHUNK: {res['text']}\n\n"
+        context += "--- END OF REPORT CONTEXT ---\n"
     
     full_prompt = f"{GREENWASHING_ANALYSIS_PROMPT}\n\n{context}\n\nUSER QUERY/COMPANY FOCUS: {query}"
     analysis_text = ask(full_prompt)
@@ -46,3 +54,64 @@ def analyze(query, query_embedding, num_results=5, num_alternatives=3):
             break
     
     return {"analysis": analysis_text, "alternatives": alternatives}
+
+def ask_gemini_with_rag(query, category=None, num_results=5):
+    embedding = get_embedding(query)
+    result = analyze(query, embedding, num_results=num_results)
+    return result["analysis"]
+
+def analyze_brand(brand_name):
+    print(f"\n{'='*60}")
+    print(f"Analyzing brand: {brand_name}")
+    print('='*60)
+    
+    try:
+        print("\n[1/3] Getting embedding for brand...")
+        embedding = get_embedding(brand_name)
+        
+        print("[2/3] Querying Chroma database...")
+        result = analyze(brand_name, embedding)
+        
+        print("[3/3] Gemini Analysis Complete!\n")
+        print("-"*60)
+        print("ANALYSIS:")
+        print("-"*60)
+        print(result["analysis"])
+        
+        print("\n" + "-"*60)
+        print("ALTERNATIVES FROM DATABASE:")
+        print("-"*60)
+        if result["alternatives"]:
+            for i, alt in enumerate(result["alternatives"], 1):
+                print(f"\n{i}. {alt['summary']}...")
+        else:
+            print("No alternatives found in database.")
+        
+        print("\n" + "="*60)
+        return result
+    except Exception as e:
+        print(f"\nError during analysis: {e}")
+        return None
+
+def scan_and_analyze():
+    from src.cv.scanner import capture_and_analyze as cv_capture
+    
+    print("\n" + "="*60)
+    print("CV + Gemini Greenwashing Scanner")
+    print("="*60)
+    print("Using camera to detect brands...")
+    
+    cv_result = cv_capture()
+    
+    logos = cv_result.get("logos_detected", [])
+    if not logos:
+        print("No brands detected. Try again!")
+        return None
+    
+    brand = logos[0].get("brand", "Unknown")
+    print(f"\nDetected brand: {brand}")
+    
+    return analyze_brand(brand)
+
+if __name__ == "__main__":
+    scan_and_analyze()

backend/src/gemini/__main__.py

@@ -0,0 +1,4 @@
+from . import scan_and_analyze
+
+if __name__ == "__main__":
+    scan_and_analyze()