change retriever name

app.py

@@ -123,12 +123,12 @@ def format_whisp_statistics(df):
 
         output = f"""🌍 **Geographic Analysis Results**
 
-## 📍 **Location Details**
-- **Country**: {country}
-- **Administrative Region**: {admin_level}
+📍 **Location Details**
+- **Country**: {country}  \n 
+- **Administrative Region**: {admin_level} \n 
 - **Total Area**: {area_text}
 
-## ⚠️ **Deforestation Risk Assessment**
+⚠️ **Deforestation Risk Assessment**
 *Risk levels are based on historical patterns, environmental factors, and land use data*
 
 - **Overall Risk**: {format_risk(risk_level)}
@@ -139,7 +139,7 @@ def format_whisp_statistics(df):
 - **Timber Extraction**: {format_risk(risk_timber)}
   *Logging and wood harvesting*
 
-## 🌳 **EUDR Compliance Analysis**
+🌳 **EUDR Compliance Analysis**
 *Based on Tropical Moist Forest satellite monitoring*
 
 **Recent Deforestation (2020-Present):** {deforestation_formatted}
@@ -147,7 +147,7 @@ def format_whisp_statistics(df):
 **EUDR Compliance Status:** {compliance_status}
 
 ---
-### 💡 **Key Insights**
+💡 **Key Insights**
 
 - **For Suppliers**: {compliance_status.split(' - ')[1] if ' - ' in compliance_status else 'Review compliance requirements carefully'}
 - **Risk Factors**: Focus on {', '.join([t.split('*')[1].strip('*') for t in [risk_pcrop, risk_acrop, risk_timber] if 'High' in format_risk(t)])} if any high-risk activities detected
@@ -200,7 +200,7 @@ def retrieve_paragraphs(query):
     """Connect to retriever and retrieve paragraphs"""
     try:   
         # Call the API with the uploaded file
-        client = Client("https://giz-chatfed-retriever.hf.space/")
+        client = Client("https://giz-eudr-retriever.hf.space/")
         result = client.predict(
                 query=query,
                 reports_filter="",

utils/generator.py

@@ -0,0 +1,56 @@
+import configparser
+
+
+def getconfig(configfile_path: str):
+    """
+    Read the config file
+    Params
+    ----------------
+    configfile_path: file path of .cfg file
+    """
+    config = configparser.ConfigParser()
+    try:
+        config.read_file(open(configfile_path))
+        return config
+    except:
+        logging.warning("config file not found")
+
+
+config = getconfig("model_params.cfg")
+PROVIDER = config.get("generator", "PROVIDER")
+MODEL = config.get("generator", "MODEL")
+MAX_TOKENS = int(config.get("generator", "MAX_TOKENS"))
+TEMPERATURE = float(config.get("generator", "TEMPERATURE"))
+
+
+def generate_response(chunks, user_query, model):
+    """
+    Generator function to produce a response text to the initial user query
+    using the retrieved document chunks and a model.
+    
+    Args:
+        chunks (list of dict): Retrieved chunks, each with 'answer' and 'score'.
+        user_query (str): The initial query text from the user.
+        model (callable or object): Language model or interface to generate text (stub).
+    
+    Yields:
+        str: Generated text response to the query.
+    """
+
+    # Aggregate text from relevant chunks for context
+    context_text = "\n\n".join(chunk['answer'] for chunk in relevant_chunks)
+
+    # Step 3: Compose prompt for model (simulate or call model)
+    prompt = (
+        f"You are an assistant responding to the query:\n\"{user_query}\"\n\n"
+        f"Use the following information to answer:\n{context_text}\n\n"
+        f"Provide a clear, concise, and informative answer."
+    )
+
+    # Step 4: Use model to generate the response text
+    # This is a placeholder; replace with actual model call, e.g.:
+    # response = model.generate_text(prompt)
+    # For demo, just yield the prompt as a stub.
+
+    # Yield the final response once
+    yield f"Simulated response based on retrieved info:\n\n{prompt}"