update

app.py

@@ -3,10 +3,9 @@ import warnings
 import re
 import os
 import google.generativeai as genai
-from google.generativeai import types
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain.schema import Document
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.prompts import PromptTemplate
 import gradio as gr
 
@@ -33,37 +32,30 @@ class ZarmaLanguageAnalyzer:
             return json.load(f)
     
     def _setup_models(self):
-        """Set up the Gemini-2.0-Flash model via Google Generative AI API."""
+        """Set up the Gemini-Flash-1.5 model via Google Generative AI API."""
         # Get API key from environment variable
         api_key = os.getenv("GOOGLE_API_KEY")
         if not api_key:
             raise ValueError("GOOGLE_API_KEY environment variable not set.")
-        self.client = genai.Client(api_key=api_key)
-        self.model = "gemini-2.0-flash"
+        genai.configure(api_key=api_key)
+        self.model = genai.GenerativeModel("gemini-2.0-flash")
         
         self.analysis_template = PromptTemplate(
             input_variables=["sentence", "grammar_check", "glossary_info"],
             template="""
             You are a Zarma language expert. Analyze this Zarma sentence: "{sentence}"
-
             Rely primarily on your expertise in Zarma grammar and meaning. Recognize proper nouns (e.g., names or places) as such unless the glossary explicitly contradicts this with a common Zarma meaning. Use the grammar check and glossary below as supplementary aids only—do not override your knowledge unless they provide clear, contextually relevant insight.
-
             Grammar check results (optional guide):
             {grammar_check}
-
             Glossary information (use it but prioritize your expertise to confirm):
             {glossary_info}
-
             Provide a detailed linguistic analysis in this exact format, with no extra text outside the sections:
-
             1. WORD BREAKDOWN:
                - [List each word with its grammatical role and meaning, e.g., "Ay: 1st person singular pronoun, meaning 'I'."]
-
             2. LINGUISTIC INSIGHT:
                - Word Order: [Describe typical Zarma word order (e.g., SOV, SVO) and how this sentence aligns or deviates]
                - Tense/Aspect Markers: [Explain tense/aspect markers like 'ga', 'goono ga', or none for past, with examples like "Ay ga koy" (I will go)]
                - Contextual Insight: [Discuss what the sentence might intend to convey and any external influences or errors]
-
             3. CORRECTNESS ASSESSMENT:
                - Is the sentence correct? [Yes/No, with explanation]
                - Reason for Incorrectness (if applicable): [Detailed reason why it’s wrong, e.g., misplaced particle]
@@ -148,7 +140,7 @@ class ZarmaLanguageAnalyzer:
             if exact_match:
                 fr_word = exact_match.get("fr", "")
                 dje_word = exact_match.get("dje", "")
-                notes = entry.get("notes", "No additional context available")
+                notes = exact_match.get("notes", "No additional context available")
                 
                 word_info[clean_word] = {
                     "french": fr_word,
@@ -256,7 +248,7 @@ class ZarmaLanguageAnalyzer:
         )
     
     def analyze_sentence(self, sentence: str) -> dict:
-        """Full analysis pipeline for a Zarma sentence using Gemini-2.0-Flash."""
+        """Full analysis pipeline for a Zarma sentence using Gemini-Flash-1.5."""
         grammar_issues = self.check_grammar(sentence)
         formatted_grammar = self.format_grammar_issues(grammar_issues)
         glossary_results = self.translate_and_explain_words(sentence)
@@ -268,24 +260,10 @@ class ZarmaLanguageAnalyzer:
             glossary_info=formatted_glossary
         )
         
-        contents = [
-            types.Content(
-                role="user",
-                parts=[types.Part.from_text(text=prompt)],
-            ),
-        ]
-        generate_content_config = types.GenerateContentConfig(
-            response_mime_type="text/plain",
-        )
-        
         raw_analysis = ""
         try:
-            for chunk in self.client.models.generate_content_stream(
-                model=self.model,
-                contents=contents,
-                config=generate_content_config,
-            ):
-                raw_analysis += chunk.text
+            response = self.model.generate_content(prompt)
+            raw_analysis = response.text
         except Exception as e:
             raw_analysis = f"Error in analysis generation: {str(e)}"
         
@@ -354,4 +332,4 @@ with gr.Blocks(title="Zarma Language Analyzer") as demo:
     )
 
 # Launch the app
-demo.launch()
+demo.launch()